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Sustainable HRM and well-being: systematic review and future research agenda

Published: 14 July 2023

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Faisal Qamar ORCID: orcid.org/0000-0003-4916-8229 1 ,
Gul Afshan ORCID: orcid.org/0000-0003-0016-5721 1 &
Salman Anwar Rana 1

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This paper attempts to undertake a systematic literature review to identify ways and means by which sustainable human resource management (HRM) and well-being are linked for better individual and organizational outcomes. Its primary focus is to study whether sustainable HRM predicts well-being at work? If yes, how and when this prediction takes place? Systematic computerized search and review were conducted for articles published until December 2022. A total of 134 research articles were finally selected. It was found that sustainable HRM predicts well-being at work. However, our findings suggest that the area is largely underexplored and empirical work is too rare. Although few moderators and mediators are examined, research is required to propose and test more comprehensive models with more robust research designs and sophisticated theoretical links.

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Data availability

Information/data of all the research papers analysed during this study are included in the body of this manuscript and its appendix. Any further information related to earlier research papers considered for this review are available from the corresponding author on reasonable request.

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Qamar, F., Afshan, G. & Rana, S.A. Sustainable HRM and well-being: systematic review and future research agenda. Manag Rev Q (2023). https://doi.org/10.1007/s11301-023-00360-6

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Human Resources Research Paper Topics For 2024

Table of contents

1.1 Human Resources Management Research Topics
1.2 Equal Employment Opportunity HR Research Topics
1.3 Career Development HR Research Topics
1.4 Research Topics on Recruitment and Selection
1.5 HR Risk Management Topics
1.6 Workplace Safety HR Topics
1.7 Trending HR Topics

Human Resources is one of the most popular and essential topics for the business minded. If you remember your basic economics, you may remember that the basic components necessary for production in any kind of economy are Land, Capital, and Labor.

Human labor is an essential resource that keeps a business running. Like any other resource, it must be managed. This is where the term “Human Resources” and Human resources research topics come in.

Having relevant data for research paper is easy if you know where to look. There are lots of online sources and books in libraries to use in your task. Make sure you spend enough time on planning before writing your task.

How to choose a Human Resources topic for your project?

Selecting research topics in human resource management is not as simple as simply choosing the title and proceeding to write it. In order to get a good grade, the paper must be original and well researched. It needs to cover all relevant aspects of the chosen HR topics. Writing a hr related research topics is a very structured and analytical process. This is true for all fields, including human resources research topics.

The first step is topic selection . This is where we can help you. This page features a list of over 90 human resources topics. If you are having problems coming up with your own ideas, please choose hr related research topics from this list instead.

These titled papers all have a great deal of material about human resource management research topics out there. They are each trending topics in hrm topics for research and have plenty of resources available out there on the internet. Each of them is also relevant to the actual field of human resources management.

So, while writing a hr related research topics is not a typical or common activity for an HR employee, it will give you a lot of insights and information. These insights could give you a leg up in the future when you have graduated from School and College.

Human Resources Management Research Topics

At most large companies, ‘Human Resources’ is an entire department of its own. Most other departments at the company typically deal with producing a good or service. Others, like the public relations department, work with the media and other external affairs. Hence, there are many ways to approach HR research topics.

How HR helps companies remain competitive in a global market.
Managing part-time, full time, and freelancing employees.
How much paid leave is optimal?
What occasions deserve raises and bonuses?
The simplest way to resolve interpersonal conflicts.
The most effective team-building strategies.
Organizing teams according to personalities.
Can an introverted employee be a good team leader?
How to improve productivity through a goal-oriented approach.
The agile method and how it helps.
The best way to utilize productivity metrics.
Methods for disciplining employees.
How to manage international employees.
Preventing workplace violence.
Benefits of regular psychological counseling for all employees.

Need help with your research paper? Get your paper written by a professional writer Get Help Reviews.io 4.9/5

Equal Employment Opportunity HR Research Topics

Are women more likely to get paid less for the same position as a man?
Do men and women deserve the same pay?
How to manage equal opportunity employment?
The best tactics for implementing equal opportunity.
Recruiting as an equal opportunity employer.
How to recognize and manage discrimination in the workplace.
The glass ceiling and how to break it.
Best practices for mediating disputes between employees.
Dealing with intimate relationships between employees.
How to create a diverse workplace?
Making the workplace an inclusive and accessible place for disabled employees.
Preventing unfair discrimination against LGBT+ employees.
The costs of an unequal workplace.
The benefits of a diverse and inclusive workplace.
Government requirements for equal opportunity.

Career Development HR Research Topics

Those who are interested in working in the field could take their first steps by writing a paper on human resource management topics. There is a huge variety of possible human resource topics for research papers, so it is likely that everyone will find some aspect of it they enjoy.

Creating leaders among employees.
Why does professional career development matter?
How career development helps both employees and organizations.
The best approaches to on-the-job training.
Should training be prioritized over completed current work?
Best practices for training interns.
Should interns be paid more?
Professional certification training for employees.
How does active professional development affect productivity?
Is it worth it to help an employee develop if they find a new, better-paid job afterward?
Skills that all employees should develop.
Must-have training and development for all employees.
Advantages and disadvantages of paying for an employee’s professional training.
Advantages and disadvantages of leading professional development sessions.
Should companies help employees pay for school?

Research Topics on Recruitment and Selection

Studying human resources is a crucial part of management studies. Whether you are a college or university student, you can buy paper online to save time and effort. There are lots of reputable services that can provide excellent assignments to boost your academic performance.

What does the ideal new employee look like?
When is the best time to recruit a new employee?
When is the worst time to recruit a new employee?
Should highly skilled but untested individuals be recruited for senior positions?
Best practices for improving employee retention.
How to attract good employees?
The best platforms to recruit on.
Is social media an effective way to recruit?
What kind of employees should small businesses look for?
What kind of employees are needed for a large company?
Criminal background checks – Do’s and Don’ts.
How to effectively assess skills during an interview.
How does HR evaluate a potential new recruit?
Is it better to recruit an employee with experience but no skill, or the other way around?
Recruiting university graduates directly – a good idea or a bad one?

HR Risk Management Topics

With so many moving parts working together in one company, it is natural for confusion or conflicts to arise. In order to make sure all these departments, employees, and managers work together, Human Resources is essential. In companies with hundreds of employees, their job simply cannot be understated.

What kind of risks does HR have to manage?
What role does HR take in risk management?
How does HR ensure worker protection?
Is HR there to protect employees or protect the company?
Legal measures HR can take.
Risk management during the covid-19 pandemic.
How HR managed risks revolving around covid-19.
Reasons to carry out regular internal audits.
Risk management among the ‘#metoo’ movement.
Training the workplace to minimize potential risks.
Risk management when working from home.
Ways to ensure all your employees follow masking and social distancing rules.
Ways to ensure all employees get vaccinated.
Responding to a legal action taken by an employee.
When should HR take legal action?

Workplace Safety HR Topics

How to ensure compliance with workplace safety rules.
The consequences of not following workplace safety.
Ways to prevent osha violations.
How to ensure all employees follow health and safety protocols?
How to ensure all employees get vaccines?
Fines and penalties for violating workplace safety rules.
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A Study of the Impact of Strategic Human Resource Management on Organizational Resilience

Jingjing yu.

1 Business School, Shandong University, Weihai 264209, China

Lingling Yuan

Guosheng han.

2 School of Economics and Management, Harbin Institute of Technology, Weihai 264200, China

3 School of Public Health and Management, Binzhou Medical University, Yantai 264003, China

Organizational resilience is a key capability for modern firms to survive and thrive in the VUCA environment. The purpose of this study is to investigate the mechanism of strategic human resource management on organizational resilience and the mediating and moderating roles of self-efficacy and self-management, respectively, in the relationship between the two. A total of 379 valid questionnaires were obtained from employees of Chinese companies in August 2022, and the data were analyzed using SPSS 22.0 and Amos. The results showed that strategic HRM can effectively contribute to organizational resilience; self-efficacy plays a mediating role in the relationship between strategic HRM and organizational resilience; self-management can effectively contribute to the impact of self-efficacy on organizational resilience; and self-management can hinder the ability of strategic HRM to contribute to organizational resilience. This paper breaks with the previous literature that studied organizational resilience from a single perspective by studying organizational resilience from the perspective of strategic human resource management (SHRM) and verifies that SHRM can be a possible path for Chinese firms to improve organizational resilience.

1. Introduction

In recent years, unexpected events such as natural disasters, financial crises, industrial accidents, trade embargoes, and even terrorist attacks have occurred frequently, and sudden “black swans” and “gray rhinoceroses” have led to a business environment that has become more full of volatility, uncertainty, complexity, and ambiguity (VUCA) [ 1 , 2 ]. The still-unresolved novel coronavirus epidemic has caused more than 40% of Chinese companies to lose money or suffer severe losses, and employees to lose their jobs or take pay cuts [ 3 ], which has a major negative impact on China’s social and economic development. It also revealed that many Chinese companies are “rigid” but “not resilient”. Organizational resilience is the core capability of today’s enterprises to cope with crises in a volatile, uncertain, complex, and ambiguous (VUCA) market environment. It helps companies to remain sensitive and adaptable to the external environment and to recover and bounce back quickly from the challenging impact of adverse events. Additionally, in the process of reflection and improvement, it goes against the trend to become the key to gain core competitiveness and even steady progress [ 4 , 5 , 6 ]. Thus, how to enhance organizational resilience in a dynamically changing business environment has become a hot topic for entrepreneurs and scholars to address [ 7 ].

A review of the literature related to organizational resilience at home and abroad reveals that the current research on organizational resilience in China is still in its infancy. The research is mainly concerned with the elaboration of the concept and principles of organizational resilience, and the scarce literature on the antecedents of organizational resilience are studied from one aspect, such as management methods, human capital, social capital, business environment, organizational system, etc. [ 8 ]. Organizational resilience is a reflection of an enterprise’s comprehensive ability to cope with an uncertain environment, and the study of a single factor cannot comprehensively explain the formation of the mechanism of resilience in an enterprise and cannot well integrate the impact of the interaction of various factors on organizational resilience, which lacks operability in enterprise practice and cannot effectively help enterprises to improve organizational resilience [ 9 ]. At present, there is an urgent need to study the formation mechanism of organizational resilience from a holistic perspective considering the comprehensive effect of each antecedent factor and provide a theoretical basis for Chinese enterprises to improve organizational resilience. Strategic human resource management is a system, process, or measure consisting of a series of temporal activities taken in order to fit with the organization’s strategy and long-term development goals and thus maintain competitive advantage. Lengnick-Hall [ 10 ] believes that organizational resilience works through the knowledge, skills, abilities, and other attributes of people within an organization, and that strategic human resource management can be achieved by changing management styles, processes, practices, and HR policies, etc. to develop these qualities in employees to enhance organizational resilience.

In exploring the role of modeling the relationship between strategic human resource management and organizational resilience, this paper found the “key” of self-efficacy, drawing on the literature on emotional competence. All HR policies and plans in an organization require employees to take specific actions to achieve the desired goals. Thus, self-efficacy, an essential emotional competency on the part of employees, is critical to the effectiveness of plan implementation. Early warning capability, flexibility during a crisis, and learning and growth capability following a crisis are three core competencies included in organizational resilience [ 11 ]. Self-efficacy refers to a strong belief in one’s own ability to comply with a corporate strategy, which enables employees to relieve psychological pressure in time, stabilize the psychological reactions of personnel to adversity, actively obtain environmental resources and external support to optimize human resource allocation, and integrate human resource management practices with corporate strategy organically by focusing on internal personnel selection and appointment, performance evaluation and assessment, and external active recruitment; this approach allows organizations to shape a corporate culture of overcoming difficulties, create dynamic and flexible adaptation mechanisms, construct internal knowledge structures, and actively seek foreign environmental resources, thereby enhancing corporate resilience capabilities by providing solutions to crises and addressing corporate structural problems [ 12 ]. Thus, self-efficacy is an essential perspective for the study of organizational resilience that can explain why strategic HRM are able to influence organizational resilience capabilities; therefore, this study intends to discuss the relationship between strategic HRM and organizational resilience as mediated by self-efficacy.

In addition, we cannot ignore the key issue that strategic HRM must be implemented and accomplished through corporate employees no matter what policies are formulated, and corporate organizational resilience capabilities must also function through corporate employees as mediators. Thus, studying the mechanism of strategic HRM’s effect on organizational resilience is inevitably influenced by employees’ work style. As a result of the rapid development of the economy and excellent material abundance of China, employees’ sense of autonomy and their self-working ability are becoming increasingly prominent. According to the traditional HRM model, it is difficult for leaders to supervise and constrain employees. It is more practical to study the effects of strategic HRM with respect to enhancing organizational adaptability and flexibility to the environment from the perspective of employees’ sense of autonomous work. Therefore, this paper uses self-management as a moderating variable to determine whether self-management plays a moderating role in the relationship between strategic HRM and organizational resilience.

According to a report by Fortune magazine, the average life expectancy of small enterprises in China is 2.5 years, and the average life expectancy of large enterprises is 7-8 years, which is far behind those of European and American countries. From the side, it shows that Chinese enterprises lack the ability of resilience to cope with the crisis. Especially under the impact of the novel coronavirus epidemic, many enterprises have experienced serious losses or even bankruptcy, resulting in employee pay cuts and unemployment, adding a heavy burden to China’s social stability and economic development. In summary, this paper takes conservation of resources theory and self-cognitive theory as the theoretical basis and empirically investigates the intrinsic mechanism of strategic human resource management on organizational resilience. It provides references for Chinese companies to enhance organizational resilience.

The main contributions of this study include two main aspects. On the one hand, this study can enrich the literature related to the study of strategic human resource management and organizational resilience; on the other hand, the research results of this paper can guide the managers of Chinese enterprises to formulate strategic human resource planning, coordinate all resources of human, financial and material resources, optimize enterprise processes, improve enterprise management policies, increase enterprise innovation, etc., so as to enhance organizational resilience.

2. Literature Review and Research Hypothesis

2.1. strategic human resource management and organizational resilience.

Strategic HRM was developed to facilitate the strategic management of organizations, and Wright and McMahan [ 13 ] give a more representative definition of this concept. They consider strategic HRM to represent an organization’s plans for human resource deployment and behavioral norms with the aim of achieving the organization’s goals. This definition emphasizes both the vertical and horizontal fit of strategic HRM: vertically, strategic HRM refers to the match between and mutual adaptation of HRM practices and the organization’s strategic management process, whereas horizontally, strategic HRM emphasizes the coherence among various HRM practices based on the planned action model. The vertical and horizontal fit of strategic HRM ensures that HRM is fully integrated into strategic planning to guarantee that HR policies and practices are generally accepted and widely used by managers and employees, such that companies can obtain inimitable or alternative competitive advantages by leveraging their HR strengths [ 9 , 14 ]. According to conservation of resources theory, strategic HRM, as a strategic organizational resource, represents an organic combination the talent resource elements in an organization as well as the allocation of resources among members of the organization; thus, strategic HRM emphasizes the flexible adjustment of staffing policies and practices, training and development programs, performance standards, selection criteria, and rewards and punishments in response to changes in external contexts, thus providing strategic tools to promote resource integration, crisis prevention and control, and learning and innovation in organizations [ 15 ].

The concept of resilience originated in the fields of physics, ecology, and environmental science, and Meyer [ 16 ] first introduced this concept into the field of management, thereby opening up a new chapter in the study of organizational resilience, which was quickly and widely studied in the fields of crisis management, disaster management, and high-reliability organizations. Previous research on organizational resilience has been focused on two main research perspectives: the rebound perspective and the rebound + overtake perspective [ 10 ]. The rebound perspective views organizational resilience simply as the ability of the organization to recover from an accident, stress, or crisis to return its original state, i.e., the ability of an organization to take countermeasures to return to its precrisis level of performance. The rebound + overtake perspective views organizational resilience not merely as the organization’s ability to respond to challenges and changes to return to its original state but also as the organization’s ability to develop new capabilities or create new opportunities for the organization to continue to thrive and grow [ 17 ]. This paper considers organizational resilience to be a dynamic and flexible organizational capacity that allows organizations to survive, adapt, recover, and even return to prosperity in an adverse environment. Lengnick [ 18 ] claimed that organizational resilience capacity is rooted in the psychology and behavior of individual employees. Employees’ knowledge, skills, and strengths regarding their values, mindsets, levels of stress tolerance, and innovation abilities are essential sources of organizational resilience [ 18 , 19 ]. These employee qualities and capabilities are closely related to the individual’s ability to adapt to dynamic environments and to develop creative solutions to resolve crises. Employee resilience is an important source of and foundation for organizational resilience; thus, organizations can enhance their organizational resilience capabilities by developing employee resilience.

In this paper, we argue that strategic HRM can influence individual resilience and thus enhance organizational resilience via the development of HRM policies and practices that match both the external environment and organizational goals [ 20 ]. Specifically, the effects of HRM policies and practices on organizational resilience can be elaborated in terms of three aspects of human resources: human capital, social capital, and psychological capital [ 15 ].

First, human capital primarily includes the physical quality and physical health of employees on the one hand and the knowledge, skills, and experience possessed by employees on the other hand. In a crisis, members of an organization can make timely judgments and actions based on the knowledge, skills, and experience they possess to change the organization’s passive situation as much as possible, thus influencing its resilience [ 19 ]. The exchange of knowledge and experience among organizational members and their interactions can promote the formation of the collective cognition of the organization. This collective cognitive ability encourages organizational members to cooperate tacitly, trust each other, and unite in the face of adversity, thus developing the unique organizational ability of the enterprise to cope with crises and affecting the organizational resilience ability [ 21 ].

Second, social capital is a potential resource possessed by the organization within the social network system, which is essentially an environmental factor that is mainly divided into internal environmental factors (e.g., colleague relationships, learning atmosphere, team spirit) and external environmental factors (e.g., partnership with suppliers or distributors, flexible external information system) [ 22 ]. Social capital can increase the levels of coordination and cooperation that employees exhibit in their work, which in turn increase the motivation and efficiency of the organization with respect to coping with a crisis. Moreover, social capital can be used to obtain resources and information from the external environment that are critical for crisis resolution and the reallocation of resources both inside and outside the organization, thus enhancing organizational resilience and mitigating the negative impacts of the crisis for the organization.

Finally, employees with high psychological capital can withstand the tremendous pressure entailed by a crisis and face challenges and changes with a positive and confident attitude, create a good organizational climate, and apply their knowledge and skills based on the local conditions to create opportunities for the organization to survive and grow in the face of adversity, which has a significant impact on the organization’s ability to enhance its resilience and obtain competitive advantage [ 23 ]. In addition, previous studies have demonstrated that strategic HRM that is well matched with the organizational environment, strategic planning, and corporate culture is closely related to organizational resilience. For example, Shafer et al. [ 23 ] found that when organizational HR practices are aligned with organizational values, organizations can promote organizational agility through staffing policies, personnel training, career development programs, and performance standards, thereby enhancing organizational resilience. Okuwa [ 24 ] found positive relationships among training, human resource development, and organizational resilience. Mienipre [ 25 ] found that talent management was significantly correlated with organizational risk monitoring and crisis response capacity. In summary, the following hypothesis is proposed.

Strategic human resource management has a positive effect on organizational resilience.

2.2. The Mediating Role of Self-Efficacy

The concept of self-efficacy was introduced by Bandura [ 26 ], an American psychologist who believed that self-efficacy represents an individual’s subjective evaluation and perception of his or her abilities, which in turn influences the individual’s behavioral choices, beliefs regarding success, and level of effort, and can to some extent determine the individual’s ability to fulfill the requirements of a particular job; that is, self-efficacy is dynamic and can change due to different levels of access to external resources, the acquisition of new knowledge and skills, or an increase in experience. According to previous studies, the factors affecting self-efficacy mainly include the following. (1) Individuals’ ability levels are evaluated prior to performing certain activities; individuals evaluate their own ability based on their past successes or failures, such that individuals who exhibit a strong sense of self-efficacy do not deny their ability due to occasional failures but rather search for the causes of environmental factors, strategies, and experiences and adjust their future actions accordingly. (2) Individuals who observe the behavior of others and encounter people with similar abilities who have achieved success can greatly enhance their own self-efficacy and increase their firm belief in achieving success. (3) Individuals receive evaluations, encouragement, and self-motivation from others. Evaluations or encouragement based on the facts of the situation can increase the individual’s belief in his or her ability to accomplish the goal. (4) The individual’s own emotional and physiological state also affects self-efficacy, such as the ability to remain calm under tremendous pressure, avoid exhibiting arrogance, analyze the pros and cons of the actual situation, and make the most appropriate decision, which can increase the individual’s ability to accomplish the goal as well as his or her sense of self-efficacy [ 26 ].

Self-efficacy is an important component of human capabilities that can influence individuals’ perceptions, ways of thinking, motivation, and actions [ 27 ]; in addition, it varies with people’s knowledge and external environment [ 28 ]. Thus, organizations can improve employees’ self-efficacy by implementing human resource practices such as communication, training, sharing successful experiences, and providing opportunities for success. For example, organizations can increase employees’ relevant work experience by providing training and organizational learning [ 29 ]. When employees are trained in job-related practices, they are able to acquire relevant job knowledge and information that can enhance their self-efficacy to perform their jobs competently. Second, employees’ self-efficacy can be stimulated by sharing the successful experiences of colleagues with similar abilities to enhance their beliefs in their ability to overcome specific job difficulties and their efforts to do so, thus moderating the empowerment of employees and providing them with opportunities to grow and succeed to ensure that employees feel supported by the organization and trusted by their leaders; this approach increases employees’ sense of organizational belonging and self-efficacy, thus allowing the organization to take full advantage of employees’ knowledge and skills and to face challenges and cope with stress actively. Strategic human resource management refers to the alignment of organizational strategic planning with human resources, which is used to guide human resource practice activities and is frequently considered to be an essential factor influencing the cognitive, motivational, and affective processes of self-efficacy [ 30 ]. Organizations can ensure sound planning and develop action plans for future operations by engaging in HR activities such as training, sharing successful experiences, role models or motivation, and developing employees’ confidence in dealing with dynamic environmental challenges and complex work. In summary, the following hypothesis is proposed.

Strategic HRM has a positive effect on self-efficacy.

According to conservation of resources theory, self-efficacy, as an essential psychological resource, is closely related to employees’ self-beliefs and can motivate them to accept challenges and persevere in the task of accomplishing their work goals [ 31 ]. Thus, when facing complex tasks, on the one hand, self-efficacy can strengthen employees’ determination and confidence to complete tasks and allow them to unite their colleagues actively, integrate relevant resources and information, and courageously face difficulties and challenges [ 32 ]; on the other hand, self-efficacy can motivate employees to self-regulate in a timely manner, relieve tension and anxiety, and reallocate resources and set goals based on the specific situational conditions at hand to ensure that difficulties can be broken down into simple goals and achievable work objectives [ 33 , 34 , 35 ]. In addition, employees who exhibit a high sense of self-efficacy are skilled at using new methods and ideas to solve unconventional problems, thus enabling the organization to find alternative ways of surviving situations of adversity and contributing to the organization’s resilience [ 35 ]. In conclusion, self-efficacy enables employees to believe in their ability to work in situations of adversity, recover quickly from anxiety, and invest the necessary effort and creativity to accomplish challenging tasks. Therefore, the following hypotheses are proposed.

Self-efficacy has a positive effect on organizational resilience.

Self-efficacy plays a mediating role in the relationship between strategic human resource management and organizational resilience.

2.3. The Moderating Role of Self-Management

According to self-cognitive theory, individuals have certain values, beliefs, knowledge systems, and behavioral norms. Individuals form their unique control systems based on these internal resources and accordingly set goals, engage in self-assessment, and exhibit self-motivation as a means of guiding their work activities, i.e., self-management [ 36 ]. The awareness of the practice community that organizational control and supervision must be achieved by influencing the self-management system to achieve this goal, i.e., by harmonizing organizational control and individual motivational orientation, is increasing [ 37 ]. Self-management refers to the process by which employees set goals, take positive actions, and engage in a series of behaviors, including self-monitoring and evaluation as well as self-reward and punishment, to promote their own intrinsic self-worth based on their personal needs. Self-management results from the interaction of individual cognition, behavior, and the external environment. Bandura [ 38 ], in developing social cognitive theory, proposed that individuals exhibit self-rationality, that is, that the individual’s response to the external world is not mechanical and passive but rather represents a form of goal-oriented behavior following self-regulation of and self-reflection on their activities; in addition, the achievement of such a goal can allow the individual to obtain self-worth and meaning (such as monetary or spiritual rewards, social needs, or self-actualization). Bandura’s model of individual self-management [ 39 ] includes three components: self-observation, self-assessment, and self-response. The process of self-observation involves actively identifying the quality, quantity, and frequency of the performance accomplishment of other individuals and comparing those individuals with oneself to make an objective assessment of one’s own work ability; the process of self-assessment entails comparing one’s actual performance with the company’s performance standards, thereby assessing one’s own performance and developing strategies for improvement; and the process of self-reaction implies rewarding and punishing oneself according to the results of the assessment as well as reflecting on and improving oneself continuously.

According to conservation of resources theory, organizational resilience is an essential intangible resource that allows the organization to survive and develop in adverse situations, thus enabling organizations to make decisive decisions in dynamic situations, flexibly deploy their internal and external resources, and take appropriate actions to ensure that the organization is always able to adapt to the business environment and obtain competitive advantage [ 11 ]. In times of crisis, only if the organization is united, determined, and confident can it seize the fleeting moment, make decisive decisions, and act efficiently to take full advantage of its own resilience. A high level of self-management ability on the part of employees, with good self-cognitive ability and the ability to obtain and process environmental information to ensure that they can quickly judge the situation in times of crisis and work in an orderly manner based on the situation, is target management. Thus, a high level of self-management can increase employees’ sense of psychological security and self-efficacy in times of crisis, thereby enhancing the resilience of the organization. Specifically, on the one hand, the process of self-management is driven by employees’ intrinsic values, and the achievement of the organization’s goals is a testament to employees’ self-worth [ 40 ]. Employees view the challenges presented by adversity as opportunities to prove their own ability and value. They view work as their responsibility and believe in accomplishing challenging goals by taking full advantage of their professional skills and creativity and working with other members of the organization to deal with environmental challenges and smoothly survive crises, thereby enhancing organizational resilience. On the other hand, employees with high levels of self-management are skilled at assessing their own abilities and performance levels or those of others as well as setting reasonable work goals and developing reasonable action strategies based on the resources and information that they obtain because employees who are skilled at self-management tend to take the initiative to collect and process environmental information, remain sensitive to the external environment and organizational operations, and ensure that they are always needed by the organization as a means of maintaining their competitive positions in the organization. In times of crisis, when the organizational landscape changes, employees quickly orient themselves to their goals with self-management ability, integrate their accessible resources, develop reasonable action plans and smoothly execute them, reduce their confusion and anxiety, increase their self-efficacy, and take full advantage of organizational resilience [ 41 ]. In conclusion, self-management can enhance the contribution of self-efficacy to organizational resilience. In summary, the following hypothesis is proposed.

Self-management plays a positive role in regulating the impact of self-efficacy on organizational resilience.

Previous studies have shown that the application of self-management in organizations can reduce the costs of business supervision and management and improve business performance and employee well-being, among other effects [ 42 ]. However, self-management is not effective in all situations [ 43 ]. Employee self-management is based on mutual trust between leaders and employees, such that leaders trust employees to be capable of accomplishing the established goals, and employees trust that they will receive set rewards for accomplishing such goals [ 44 ]. However, critical moments that require companies to demonstrate their resilience to cope with difficult times can lead to changes in companies’ human resource management plans and thus in their goals and development direction as well as the reshuffling of personnel rights and interests within such companies. In this situation, employee self-management hinders the ability of strategic human resource management to promote the company’s organizational resilience capabilities. Specifically, first, according to conservation of resources theory, the achievement of self-management goals requires the input of individual and organizational resources [ 45 ]. Crises cause the achievement of goals to be rife with uncertainty. Employees have negative attitudes toward the implementation of the company’s strategic human resource plan and corporate goals due to their desire to prevent their resources from being lost. In addition, organizational resources become scarcer and more difficult to acquire in a crisis. Employees tend to compete for internal resources to maintain their existing resources and rights, which strains the relationships among people within the organization and is not conducive to communication, cooperation, and knowledge sharing among members of the organization. In contrast, organizational resilience requires a high degree of team cohesion, mutual trust, assistance, and cooperation and thus is not conducive to organizational resilience. Second, the adjustment of strategic HR policies in times of crisis can lead to the reformulation of individual goal management. Employees’ internal self-actualization and self-growth are essential drivers of self-management goals. Once corporate goals deviate from individual goals, employees’ actions may impede or even prevent the implementation of strategic HRM plans, thus rendering the organization unable to deploy people and resources rapidly and perhaps even causing the organization to miss the best time to act, which is not conducive to the development of organizational resilience [ 46 ]. Finally, strategic HRM is a management approach that aligns HRM with corporate strategy. Corporate strategy often takes the form of management involving multiple goals, such as corporate performance, social responsibility, and brand image. The complexity of work and teamwork cause corporate goals to become indistinguishable or unclear, which makes it difficult for employees to set and implement their personal self-management goals, thereby causing them to become confused and uncomfortable and to experience self-doubt or negative emotions, which is not conducive to the development of organizational resilience. In summary, the following hypothesis is proposed.

Self-management negatively affects the impact of strategic human resource management on organizational resilience.

In summary, based on conservation of resources theory and self-cognitive theory, this paper constructs a moderated mediation model, as shown in Figure 1 , and examines the relationships among strategic human resource management, self-efficacy and self-management, and organizational resilience.

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Hypothetical model of the mediating effect of self-efficacy and the moderating effect of self-management.

3. Methodology

3.1. study sample.

This study was conducted to investigate the organizational resilience of enterprises within China. In order to guarantee the accuracy, reliability of data, and wide distribution of the research sample, this study follows the principle of randomness to select the employees of enterprises with different industries, ages, education levels, positions, and income statuses in several cities within China. Due to the special national conditions of China, there are major disparities in the development levels of various regions, so the sample source of this paper includes developed large cities, such as Beijing, Shanghai, Shenzhen, Guangzhou, etc., medium development level cities, such as Jinan, Qingdao, Dongguan, Huizhou, Haikou, Lanzhou, etc., and developing small cities, such as Jiuquan, Weihai, Hami, etc. For the convenience of sample collection, a combination of online and on-site distribution was chosen for this study. In order to guarantee the authenticity and accuracy of the acquired data, a partial reverse setting of the question items was used. It was filled out voluntarily and anonymously to reduce the concerns of those who filled it out. A lottery link was also included with the questionnaire to incentivize the completion of the questionnaire. A total of 441 questionnaires were collected for one month starting from August 2022, excluding invalid questionnaires that were completed too quickly, filled out incorrectly, had omitted answers, or were duplicates. 379 valid questionnaires were obtained, for a return rate of 86%. The gender distribution of the sample was 53% males and 47% females; the age distribution included 16.9% of participants aged 25 and below, 37.2% aged 26–35, 28.5% aged 36–45, 14.8% aged 46–55, and 2.6% aged 55 and above; the education distribution included 20.6% of participants with a high school/junior college education, 43.3% with bachelor’s degrees, 10.3% with master’s degrees, and 2.1% with doctoral degrees.

3.2. Variable Measurement

All variables included in this study questionnaire were measured using the seven-point scale developed by Richter. All the scales used in this paper are well-established scales with good reliability and validity that have been validated many times in the Chinese cultural context. Additionally, a small sample of 73 people was taken for pre-study. Afterwards, two professors and three PhD and MSc students in the field of business management and human resource management examined and adjusted the new questionnaire according to the research questions, validity, and reliability of the questionnaire results, Chinese cultural background, and readability.

Organizational resilience: this variable was measured using a 15-item organizational resilience scale developed by Xiu’e Zhang et al. [ 47 ] in the context of China. This scale contains items such as “ability to adapt and creatively solve problems when a crisis occurs” and “ability to access needed resources quickly to address challenges in times of crisis”.

Strategic human resource management: this variable is assessed using a 19-item scale based on Delery’s Strategic Human Resource Management Scale [ 48 ], adapted to the Chinese cultural context, which contains items such as “Individuals in this job have clear career paths within the organization“ and “Individuals in this job have very little future within this organization (reverse-coded)“.

Self-management: this variable is assessed using a 10-item scale based on Renn ‘s self-management scale [ 49 ], adapted to the Chinese cultural context, including “I set specific goals for myself at work”, “I establish challenging goals for myself at work”, and “I clearly define goals for myself at work”.

Self-efficacy: this variable was measured using an eight-item scale developed by Chen et al. [ 50 ]. This scale contains items such as “I will be able to achieve most of the goals that I have set for myself” and “When facing difficult tasks, I am certain that I will accomplish them”.

Control variables: this paper investigates the effects of organizational human resource management policies, self-efficacy, and self-management on organizational resilience from the perspective of human resource management. To make the questionnaire data as accurate as possible, the gender, age, and education of employees are used as control variables in this paper to reduce the influence of errors on the analysis of the relationships among variables.

4.1. Common Method Biases Test

This study draws on Podsakoff et al. [ 51 ] to procedurally reduce homogeneous method bias by selecting different spatial survey respondents, anonymous surveys, and partial question item reversal settings at the time of data acquisition. Harman one-way analysis of variance was used to measure the presence of severe common method bias. The results of the SPSS 22.0 test revealed a total of six factors with eigenvalues greater than one for the unrotated exploratory factor analysis. Additionally, the maximum factor variance explained was 28.22%, which was much less than 40%; thus, there was no serious common method bias in this study.

4.2. Reliability and Validity Tests

First, this study used SPSS 22.0 and AMOS statistical software to analyze the data from 379 samples. Internal consistency tests were conducted based on the criteria of whether the coefficient of internal consistency was greater than 0.7 and whether the coefficient of internal consistency would increase after the deletion of a question item. The test results are described in Table 1 . The Cronbach’s α values of all variables are above 0.90, and the deletion of any question item does not increase the Cronbach’s α value significantly, indicating that the variables have good internal consistency. The CR values are all greater than 0.90, and the AVE values are all greater than 0.55. This indicates that the variables have good composite reliability.

Reliability test results for each variable.

Note: SHRM represents Strategic Human Resource Management; SM represents Self-Management; SE represents Self-Efficacy; OR represents Organizational Resilience.

Second, this study developed confirmatory factor analysis models for strategic human resource management, self-management, self-efficacy, and organizational resilience and conducted confirmatory factor analysis on the research models using AMOS. The results showed that all model indicators met the statistical benchmark values (χ 2 /df = 2.658, RMSEA = 0.066, CFI = 0.905, IFI = 0.905), thus indicating that the model goodness of fit well. In addition, the fit indices of the randomly selected two-factor model and those of one-factor and three-factor models were compared, as shown in Table 2 . The results showed that the fit indices of the original model were significantly better than those of the one-factor, two-factor, and three-factor models, thus indicating that the original model had good discriminant validity.

Results of validation factor analysis.

Note: *** denotes p < 0.001.

4.3. Descriptive Statistics and Correlation Analysis

The mean and standard deviation of each variable as well as the correlations among all the variables were analyzed using SPSS 22.0, and the results of this analysis are shown in Table 3 . There was a positive and strong correlation between strategic HRM on the one hand and organizational resilience (r = 0.722, p < 0.01) and self-efficacy on the other (r = 0.676, p < 0.01); the relationship between self-efficacy and organizational resilience (r = 0.711, p < 0.01) also exhibited a positive and robust correlation, thereby providing preliminary evidence to support the research hypotheses.

Means, variances, and correlation coefficients of the variables.

Note: ** denotes p < 0.01; SHRM represents Strategic Human Resource Management; SM represents Self-Management; SE represents Self-Efficacy; OR represents Organizational Resilience.

4.4. Test of Mediation Model with Moderation

In this paper, we refer to Wen [ 52 ] with the moderated mediation model test method to test the mediation model first, and, on the basis of significant mediation effect, we conduct the moderated mediation model significance test to verify whether each model proposed in this paper is significant.

First, this study tested the mediating effect on the relationship between self-efficacy on strategic HRM and organizational resilience using Model 4 (mediating model) in the SPSS macro developed by Hayes [ 53 ]. The results of this test are shown in Table 4 . Strategic HRM has a significant positive effect on organizational resilience (B = 0.711, t = 19.952, p < 0.001); strategic HRM has a significant positive effect on self-efficacy (B = 0.568, t = 17.180, p < 0.001); and self-efficacy has a significant positive effect on organizational resilience (B = 0.459, t = 9.098, p < 0.001). In addition, the upper and lower limits of the bootstrap 95% confidence intervals pertaining to the direct effect of strategic HRM on organizational resilience and the mediating effect of self-efficacy do not contain 0, as shown in Table 5 , thus indicating that strategic HRM affects organizational resilience not only directly but also indirectly via the mediating effect of self-efficacy, with the direct and indirect effects accounting for 63% and 37% of the total utility, respectively.

Mediated model test of self-efficacy.

Note: * denotes p < 0.05; *** denotes p < 0.001; SHRM represents Strategic Human Resource Management; SM represents Self-Management; SE represents Self Efficacy; OR represents Organizational Resilience.

Decomposition of total utility, direct effects, and mediating effects.

Second, the moderated mediation model was tested using Model 15 in the SPSS macro prepared by Hayes (2012) [ 53 ]. The results of the test are shown in Table 6 and Table 7 . After including self-management in the model, the product term of strategic HRM and self-management has a negative effect on organizational resilience (B = −0.144, t = 6.617, p = 0.01). Furthermore, the moderating effect of self-management contains 0 between the upper and lower limits of the bootstrap 95% confidence intervals at the eff1 (M − 1SD) level. In comparison, this effect does not contain 0 between the upper and lower limits of the bootstrap 95% confidence intervals at the eff1 (M + 1SD) level, thus indicating the significant moderating effect of self-management. The product term of self-efficacy and self-management positively affected organizational resilience (B = 0.137, t = 6.617, p = 0.001). Further simple slope analysis indicated that the effect of strategic HRM on organizational resilience tends to decrease gradually as the level of self-management increases and that the effect of self-efficacy on organizational resilience tends to increase in this context, as shown in Figure 2 a,b.

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( a ) The moderating role of self-management in the relationship between strategic human resource management and organizational resilience; ( b ) the moderating role of self-management in the relationship between self-efficacy and organizational resilience.

Mediated model tests with moderation.

Note: * denotes p < 0.05; ** denotes p < 0.01; *** denotes p < 0.001; SHRM represents Strategic Human Resource Management; SM represents Self-Management; SE represents Self-Efficacy; OR represents Organizational Resilience.

Direct and mediated effects at different levels of self-management.

5. Discussion

Based on the conservation of resources theory and self-cognitive theory, this study takes employees in Chinese culture as the research object and explores the mechanism and boundary conditions of strategic human resource management on organizational resilience. The three aspects of human capital, social capital, and psychological capital are explained to ensure that the human resources of a company fit with the corporate strategy to ensure that the strategic goals of the company match with the external environment, and that the internal resources are rationally allocated to promote the organizational resilience. Self-efficacy, as an emotional ability, is an employee’s attitude and belief about the company’s ability to cope with crises. Organizational resilience is a corporate soft capability embedded in employees’ knowledge, skills, and traits. Thus, employees’ beliefs about achieving strategic human resource management goals will influence employees’ performance in times of crisis and thus the ability to perform with organizational resilience. Therefore, the potential impact of self-efficacy on the performance of organizational resilience capabilities cannot be ignored. The impact of self-management on organizational resilience is uncertain. Self-management can enhance the positive impact of strategic HRM on organizational resilience but hinders the positive impact of self-efficacy on organizational resilience.

(1) Hypothesis 1, that strategic HRM in the Chinese context facilitates organizational resilience, was confirmed. Facing a VUCA environment, business operations are fraught with many uncertainties, a point which is especially salient to this study since it was conducted in the middle of the novel coronavirus pandemic, which has had a massive impact on the global economy and people’s lives. To address this major crisis that can reshape the global economic landscape, it is imperative for companies to adjust their corporate strategies and long-term development plans, encourage their employees to respond to the associated challenges actively, and transform the crisis into an opportunity for growth. The empirical study of strategic human resource management and organizational resilience in the face of crisis shows that strategic human resource management can actively transform corporate development strategies, reorganize and reallocate corporate human resources, lead companies to adapt to changes quickly, act flexibly and innovate actively, and have a positive effect on the improvement of organizational resilience. Accordingly, strategic human resource management is an effective way in which enterprises can ensure their survival and obtain competitive advantages in the face of a crisis.
(2) This study tested hypothesis 2, that strategic human resource management has a positive effect on self-efficacy, hypothesis 3, that self-efficacy has a positive effect on organizational resilience, and hypothesis 4, that self-efficacy mediates the effect of strategic human resource management on organizational resilience. Based on the argument that strategic human resource management positively affects organizational resilience, this study further argues that strategic human resource management can enhance organizational resilience by increasing employees’ self-efficacy. Self-efficacy refers to an employee’s strong belief in his or her own ability to do his or her job and accomplish the associated tasks. Self-efficacy enables employees to act rationally in times of crisis, to believe that the company has the strength to deal with the crisis, to respond positively to the company’s HR policies and practices, to unite with colleagues, and to dare to solve corporate problems in innovative ways. Self-efficacy enables the company’s strategic human resource management policies and practices to be implemented quickly throughout the company, thereby enhancing the company’s operations and flexibility in times of crisis and enabling the organization to recover quickly from a crisis and respond to a variety of environmental challenges, thus enhancing the organizational resilience that allows the organization to deal with complex environments.
(3) Hypothesis 5 was tested, that is, the positive moderating role of self-management in the effect of self-efficacy on organizational resilience. Self-management has a nonnegligible impact on the effect of self-efficacy on organizational resilience. The achievement of corporate strategic goals is ultimately based the actions taken by employees at work, and the self-management ability of employees is related to the efficiency and effectiveness of policy implementation. Employee self-management motivates employees to combine corporate goals with their own internal needs, set their own goals, actively access and use external information and resources, assess the gaps between goals and actual performance as well as the difficulties associated with crossing those gaps, and choose creative action paths to achieve their goals. Thus, self-management ability can enhance the organization’s sensitivity to the external environment, thus allowing the organization to prepare for crises in advance to ensure that employees can act with plans and goals in times of crisis, thereby enhancing their self-efficacy and making full use of their creativity and professional skills; given such preparation, the organization can smoothly survive the crisis and continue normal operations or even increase the prosperity of the enterprise.
(4) Hypothesis 6 was also tested, that is, the negative moderating role of self-management in the effect of strategic HRM on organizational resilience. Self-management negatively influences the impact of strategic human resources on organizational resilience. Previous research on self-management has focused on the positive effects of self-management on business management, such as its effects on business performance, employee satisfaction, employee happiness, and creativity. However, this study finds that employee self-management capabilities at the strategic level may be detrimental to organizational resilience. The original driving force behind the role of self-management is rooted in the deep-seated needs of employees. In times of crisis, if adjustments to corporate strategies and resource reorganizations deviate from the goal of self-management, employee self-management may impede or jeopardize the implementation and achievement of corporate strategic goals. Self-management causes the organization to become slow to act, rigid in its operations, and inflexible and insensitive in times of crisis, and it is detrimental to the development of organizational resilience.

Based on these findings, this paper argues that strategic human resource management is conducive to the enhancement of organizational resilience and is a possible way in which organizations can cope with potential crises and turbulent business environments. Strategic HRM allows companies to create innovations in their organizational staffing structures and systems actively, thereby enhancing the ability to self-repair and self-rebound at the organizational level; it allows companies to respond to the diverse and constantly changing needs of the market and customers and enhance the adaptability and flexibility of the organization, which is crucial for the organization’s competitiveness in the market.

6. Conclusions

Strategic human resource management facilitates organizational resilience capacity enhancement and is a possible path for organizations to respond to potential crises and turbulent business environments. Strategic HRM facilitates companies to actively innovate their organizational staff structure and system, enhance the ability to repair and rebound at the organizational level, respond to the diversified and changing needs of the market and customers, and enhance the adaptability and flexibility of the organization to the market. This is the reason why many companies are consciously implementing strategic human resource management. Thus, strategic HRM is a possible path for Chinese companies to enhance organizational resilience.

6.1. Theoretical Contributions

(1) This paper expands the conservation of resources theory and discusses important antecedent variables that facilitate the organization’s ability to exhibit organizational resilience. Organizational resilience is an essential resource and capability that allows companies to adapt to changes actively following a crisis, seek opportunities for survival and innovation, and overcome difficulties and achieve counter prosperity. In a dynamic and changing business environment and given human-centered management trends, it is crucial to clarify the manner in which strategic human resource management can enhance organizational resilience. Managing and utilizing the company’s employees well in a manner that takes advantage of the company’s talent and allows the company to cope with an unpredictable business environment has become a hot topic for both corporate managers and academic researchers. This paper focuses on the ways in which a human resource management model that fits with corporate strategy can enhance employees’ self-efficacy and thus organizational resilience, thereby providing a new perspective on the relationship between strategic human resource management and organizational resilience, theoretically considering possible ways of enhancing organizational resilience, and helping expand research on the mechanisms underlying the impact of strategic human resources.
(2) This paper validates the important influence effect of self-efficacy, and it explores the relationship between strategic human resource management, self-efficacy, and organizational resilience from the perspective of conservation of resources theory and self-cognitive theory, using strategic human resource management as an antecedent variable of self-efficacy, which helps to understand the intrinsic correlation between strategic human resource management, self-efficacy, and organizational resilience in depth. The mechanisms of how strategic HRM affects organizational resilience have been unclear in past previous research. This paper explores the “black box” of the relationship between the mechanisms of strategic HRM’s impacts on organizational resilience through the self-efficacy variable and highlights the vital role and value of self-efficacy in organizational resilience.
(3) This paper analyzes the theoretical mechanisms and boundary conditions according to which organizational resilience can function in crises. Regardless of the uniqueness and effectiveness of the strategies and responses that are adopted by enterprises, these strategies and responses must be implemented and facilitated by employees. Therefore, in times of crisis, enterprises should pay more attention to employees’ psychology, attitudes, and abilities, stimulate their creativity and motivation, and take the best path of action. Therefore, this paper includes self-management as a moderating variable to deepen our understanding of organizational resilience at the enterprise human resource management level. Through theoretical extrapolation and empirical research, the paper reveals that employees’ self-management is not conducive to the promotional effect of strategic HRM on organizational resilience, a conclusion which differs from the findings of many previous studies regarding the positive effects of self-management on enterprises; the paper thus argues that the promotional effect of self-management on enterprise management must have an appropriate background and conditions.

6.2. Practical Implications

Previous research has failed to answer the question of why some companies can transform themselves and survive when faced with a significant crisis, whereas others fall apart. This paper has significant practical value for understanding the ways in which strategic human resource management can help companies survive and grow in a dynamic environment by enhancing organizational resilience when faced with a crisis and uncertainty.

First, enterprises should actively guarantee that their corporate strategies match their human resource management to ensure that human resources can serve as critical capital to help enterprises survive the crisis and achieve their strategic goals smoothly. The novel coronavirus epidemic is a significant test of enterprise human resource management and continuous operation and development. Companies should optimize their corporate strategies and human resource structures continuously as part of their daily operations and should focus on the power of talent. When facing a crisis, companies should be skilled at exploring the potential opportunities associated with the challenges, thereby improving the cohesiveness of employees, taking full advantage of the creativity of employees, and skillfully using the company’s potential resources so that the company can endure the crisis smoothly; accordingly, the company should actively reflect on the problems and loopholes in the company’s operation after the crisis, further adjust the company’s strategic layout, and be fully prepared to deal with possible crises in the future.

Second, the enterprise should focus on improving employees’ self-efficacy and enhancing their work execution and enthusiasm. Employees are the primary capital of an enterprise and represent the only driving force for the creation of value. In an enterprise, human resource management should focus on adopting people-oriented management policies, cultivating employees’ self-efficacy, and allowing employees to realize that the enterprise values them. This paper explores the role of self-efficacy in enhancing organizational resilience from a practical perspective and shows that the enhancement and utilization of the enterprise’s organizational resilience capability ultimately depends on the power of its employees.

Finally, the enterprise should focus on employees’ self-management capabilities and simultaneously enhance its own internal management capabilities. Previous research has illustrated a variety of benefits of employee self-management on corporate performance. However, based on both theoretical extrapolation and practical research, this paper demonstrates that self-management is not beneficial to organizational development under all conditions. Only when employees’ self-goals and organizational goals are aligned do employees exert their utmost efforts to accomplish overall corporate goals. In management practice, managers should focus on employees’ career development plans and intrinsic needs to ensure that the organization’s strategy matches their jobs and to guarantee that their jobs meet their intrinsic needs.

6.3. Limitations and Prospects

This study employs a combination of theoretical derivation and empirical research. It achieves some success regarding both the theoretical and practical aspects of organizational resilience research, but it also faces certain limitations. First, this paper uses only the questionnaire method to obtain sample data, i.e., it relies on a single data source. Future research can employ experimental, interview, and other methods combined with a questionnaire to improve data accuracy. Second, the data used in this study were obtained from employees’ self-reports, and no attention was given to temporal changes when the respondents completed the questionnaires. Although this paper examined the possibility of common method bias using Harman’s one-way analysis of variance method, the results of which were within an acceptable range, the effect of common method bias could not be avoided entirely. Future studies can reduce common method bias by obtaining objective data from companies or enhancing the design of the study. Finally, this study explored only the mediating variable of self-efficacy. Future research can explore other mediating variables associated with the relationship between strategic HRM and organizational resilience from other perspectives with the aim of gradually improving the research on the mechanism underlying the effects of strategic HRM and organizational resilience.

Funding Statement

This research was funded by Natural Science Foundation of China grant number 42201224 and the Innovative Team Development Project of Inner Mongolia Higher Education Institutions, grant number: NMGIRT2206.

Author Contributions

Conceptualization, J.Y. and L.Y.; methodology, L.Y.; software, L.Y.; investigation, L.Y.; resources, P.L.; data curation, H.L.; writing—review and editing, G.H.; visualization, H.L.; supervision, G.H.; project administration, G.H.; funding acquisition, P.L. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Ethics Committee of Shandong University (Project identification code: 3885535).

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Research trends in human resource management. A text-mining-based literature review

International Journal of Manpower

ISSN : 0143-7720

Article publication date: 26 April 2022

Issue publication date: 14 March 2023

The purpose of the study was to detect trends in human resource management (HRM) research presented in journals during the 2000–2020 timeframe. The research question is: How are the interests of researchers changing in the field of HRM and which topics have gained popularity in recent years?

Design/methodology/approach

The approach adopted in this study was designed to overcome all the limitations specific to the systematic literature reviews and bibliometric studies presented in the Introduction. The full texts of papers were analyzed. The text-mining tools detected first clusters and then trends, moreover, which limited the impact of a researcher's bias. The approach applied is consistent with the general rules of systematic literature reviews.

The article makes a threefold contribution to academic knowledge. First, it uses modern methodology to gather and synthesize HRM research topics. The proposed approach was designed to allow early detection of nascent, non-obvious trends in research, which will help researchers address topics of high value for both theory and practice. Second, the results of our study highlight shifts in focus in HRM over the past 19 years. Third, the article suggests further directions of research.

Research limitations/implications

In this study, the approach designed to overcome the limitations of using systematic literature review was presented. The analysis was done on the basis of the full text of the articles and the categories were discovered directly from the articles rather than predetermined. The study's findings may, however, potentially be limited by the following issues. First, the eligibility criteria included only papers indexed in the Scopus and WoS database and excluded conference proceedings, book chapters, and non-English papers. Second, only full-text articles were included in the study, which could narrow down the research area. As a consequence, important information regarding the research presented in the excluded documents is potentially lost. Third, most of the papers in our database were published in the International Journal of Human Resource Management, and therefore such trends as “challenges for international HRM” can be considered significant (long-lasting). Another – the fourth – limitation of the study is the lack of estimation of the proportion between searches in HRM journals and articles published in other journals. Future research may overcome the above-presented limitations. Although the authors used valuable techniques such as TF-IDF and HDBSCAN, the fifth limitation is that, after trends were discovered, it was necessary to evaluate and interpret them. That could have induced researchers' bias even if – as in this study – researchers from different areas of experience were involved. Finally, this study covers the 2000–2020 timeframe. Since HRM is a rapidly developing field, in a few years from now academics will probably begin to move into exciting new research areas. As a consequence, it might be worthwhile conducting similar analyses to those presented in this study and compare their results.

Originality/value

The present study provides an analysis of HRM journals with the aim of establishing trends in HRM research. It makes contributions to the field by providing a more comprehensive and objective review than analyses resulting from systematic literature reviews. It fills the gap in literature studies on HRM with a novel research approach – a methodology based on full-text mining and a big data toolset. As a consequence, this study can be considered as providing an adequate reflection of all the articles published in journals strictly devoted to HRM issues and which may serve as an important source of reference for both researchers and practitioners. This study can help them identify the core journals focused on HRM research as well as topics which are of particular interest and importance.

Human resource management
Text-mining

Piwowar-Sulej, K. , Wawak, S. , Tyrańska, M. , Zakrzewska, M. , Jarosz, S. and Sołtysik, M. (2023), "Research trends in human resource management. A text-mining-based literature review", International Journal of Manpower , Vol. 44 No. 1, pp. 176-196. https://doi.org/10.1108/IJM-03-2021-0183

Emerald Publishing Limited

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

The human resource (HR) function has evolved over the years from serving a purely administrative role into one that is more strategic in character. Today it is believed that the mission of human resource management (HRM) is to support the organization in achieving its objectives by developing and implementing HR strategies that are integrated with a company's business strategy, promote staff development, foster a positive employment relationship, promote an ethical approach to people management, and care about the environment (social and natural) ( Ehnert, 2009 ; Braga et al. , 2021 ).

In practice, HRM means providing continuous solutions to a wide array of problems occurring in employee-employer, line worker-manager, and employee-employee relations and also in contacts with, e.g. trade unions. Human behaviors, feelings and attitudes are determined both by the personal characteristics of individuals and by the impact of the environment. The shape of HRM is significantly influenced by such factors as, e.g. the demographic and technological transformations ( Greiling, 2011 ; Silva and Lima, 2018 ), and globalization ( Gerhart and Fang, 2005 ).

HRM has evolved as a professional and academic discipline in parallel with both planned shifts in global considerations and unplanned phenomena such as, e.g. epidemics. For researchers it is crucial to identify, define, explain, and help practitioners understand the key factors which have an impact on HRM. Another of the researchers' roles is to formulate practical guidelines on how to manage people in different circumstances and outline areas of future research. HRM thrives on the contributions made in other fields that it assimilates and applies in practice. It unscrupulously builds on theoretical developments made earlier in related disciplines ( Boxall et al. , 2009 ). Finally, the researcher endeavors to provide an overview, comparisons, analyses and syntheses of previously published findings ( Paul and Criado, 2020 ).

The theme of trends in HRM has been addressed in numerous publications (e.g. Cooper et al. , 2020 ; Madera et al. , 2017 ). Their authors have employed various approaches to identify such phenomena, including systematic literature reviews. Articles offering a traditional overview provide a quantity-oriented (i.e. meta-analytical, systematic) approach together with descriptive or qualitative elements. Jointly, they develop a theoretical background, highlight irregularities in existing findings, integrate the findings of a wide variety of publications and in general provide other researchers with an up-to-date understanding of the discipline, frequently prepared by leading specialists ( Palmatier et al. , 2018 ). In most cases, the documents selected for analysis were based on titles, keywords and abstracts only. Unfortunately, they contain only around 8% of all research findings ( Blake, 2010 ). In order to gain a deeper insight into such a body of knowledge authors have often turned to the by-hand review method (e.g. Cooper et al. , 2020 ).

Conventional systematic by-hand literature reviews are sometimes characterized by errors in article selection, possible simplifications and potentially incomplete and not universal results (subjective, impressionistic descriptions), In response to these shortcomings, in recent years a number of new alternatives have emerged. One new approach that has attracted increasing attention is bibliometric studies. This method applies dedicated IT tools to gauge trends in articles. They examine academic material from both an objective and qualitative perspective for the purposes of identifying, organizing, and analyzing information in a specific research field ( Capobianco-Uriarte et al. , 2019 ). As far as trends in HRM are concerned, Markoulli et al. (2017) presented a summary of previously published traditional and narrative reviews and on its basis created a science map and defined clusters based on keyword co-occurrence analysis and the VOSviewer software tool.

Bibliometric analyses can be treated as a platform for writing an entire article or can be used only as preparation for the groundwork for further in-depth content analysis and qualitative descriptions. In turn, a text mining toolset can help identify research trends and select papers which are in line with a particular trend. Moreover, a full-text analysis of publications using a text mining toolset enables researchers to obtain higher-quality results than when using only keywords, such as in the case of VOSviewer analyses ( Kobayashi et al. , 2018 ). As a consequence we decided that it was worth adopting a methodology based on full-text mining and a big data toolset in order to identify trends in HRM research. We believe that big data and analytics help not only companies function but also researchers in a highly data-driven world ( Kobayashi et al. , 2018 ).

The purpose of the study was to detect trends in HRM research presented in journals during the 2000–2020 timeframe. The following research question was asked: how are the interests of researchers changing in the field of HRM and which topics have gained in popularity in recent years?

The paper is organized as follows. In the second section we describe the HRM research trends identified in previous studies. Here the focus is on the context in which authors were operating when analyzing HRM issues. The third section is devoted to the research method employed for the purpose of this study. Then we present the results and discussion. The article ends with conclusions, including limitations and areas of future research.

The article makes a threefold contribution to academic knowledge. First, it uses modern methodology to gather and synthesize HRM research topics. The proposed approach was designed to allow early detection of nascent, non-obvious trends in research, which will help researchers address topics of high value for both theory and practice. Second, the results of our study highlight shifts in focus in HRM over the past 20 years. Third, the article suggests further directions of research.

2. Trends in the HRM research identified in previous studies

In their search for HRM research trends authors of this study firstly used the Scopus database and a search strategy based on such terms as: trends in human resource management/HRM, trends in research on human resource management/HRM, human resource management/HRM trends, intellectual structure of human resource management/HRM. The searching process covered titles, abstracts and keywords and was limited to articles written in English. The search produced 37 documents. Then the authors also searched for additional articles in Google Scholar.

Most of the articles were devoted to the trends identified in HR practices in companies (e.g. Dubravska and Solankova, 2015 ). One of such trends is HRM digitalization ( Ashbaugh and Miranda, 2002 ). Table 1 presents a list of HRM trends identified in the research (related to academic work) conducted by different authors.

It can be concluded from the above that researchers employed different approaches to defining and identifying these trends. Research trends may be associated with research topics (e.g. Özlen, 2014 ), research methods (e.g. Pietersen, 2018 ) and the general characteristics of the academic domain (e.g. Sanders and De Cieri, 2020 ). Although a number of authors have provided traditional literature reviews of trends in HRM, Chae et al. (2020) , for example, focused only on the local (Korean) research trends and used only keyword analyses. Others focused on a specific sector ( Cooper et al. , 2020 ), industry ( Madera et al. , 2017 ) or region ( Wood and Bischoff, 2020 ). There are also articles that outline the evolution of research in particular journals (e.g. Pietersen, 2018 ). Others address specific problems, such as international HRM (e.g. Sanders and De Cieri, 2020 ) or green HRM ( Yong et al. , 2020 ). The most visible trends identified in previous studies and associated with research topics were strategic HRM, HR performance and employment/industrial relations. The first topic was addressed in eight works while the remaining was the subject of five publications.

3. Material and methods

The approach adopted in this study was designed to overcome all the limitations specific to the systematic literature reviews and bibliometric studies as presented in the Introduction. The full texts of papers were analyzed. The approach applied is consistent with the general rules of systematic literature reviews ( Tranfield et al. , 2003 ) and consists of several steps, which are presented in Figure 1 .

3.1 Selection of journals

Thousands of articles covering HRM can be found on both Scopus and the Web of Science. For the sake of the present analysis, it was necessary to define inclusion criteria in the meta-analysis.

The main topic of the journal was related to HRM,

The journals were indexed in Scopus and WoS,

The journals have a high SNIP index value (the limit value is set at 1 - status for 2020; full values are presented in Table 2 ),

Full versions of the article were available,

The articles were published in the years 2000–2020.

A total of 8 journals met the above criteria ( Table 2 ). The full texts of the papers were downloaded from academic databases. No duplicates were found. Only research papers were included, while editorials, calls for papers, errata and book reviews were excluded.

All the metadata were removed from the papers. The titles and abstracts often contain catchwords designed to increase readership. As a consequence, only the texts of papers minus their titles, keywords, abstracts and references were analyzed in this study. Additional bibliographic information that could be useful in the analytical process was downloaded from the Crossref database. Each paper was converted into a text file and then into a bag-of-words model for the needs of automatic analysis using computer algorithms. The algorithms were created using Python libraries, such as grobid, nltk, scikit-learn, hdbscan, and scipy ( Pedregosa et al. , 2011 ).

3.2 Search for the most important terms

w i j – result for term i in document j ,

t f i j – number of occurrences of i in j ,

d f i – number of documents containing i ,

N – number of documents in the corpus (set of documents).

The TF-IDF method is not a mathematical model. It requires extensive computation, cannot be used to discover synonyms and ignores multiple meanings of words ( Zhang et al. , 2011 ). However, in the case of research papers, these problems have a minimal impact due to the more precise language used by researchers.

3.3 Identification of thematic groups (clusters)

The TF-IDF model presents each paper as a multidimensional vector. The number of dimensions is equal to the number of keywords used in the analysis. In the next step, all the vectors were compared to each other, which led to the discovery of clusters.

As mentioned in section 3.2 , the TF-IDF model does not analyse synonyms and ignores multiple meanings of words and phrases. In scholarly texts, it is rarely a problem. Even in HRM, where the number of synonyms can be perceived as higher than in other areas of management, the impact on the results should be negligible.

There are two main approaches to clustering: partitioning and hierarchical clustering. The former can be applied when all the corpus elements must be included in one of the groups. This induces data noise, as not even similar elements have to be included. The latter allows some elements to remain outside the clusters. The clusters become much more homogenous. This constitutes a better approach when it comes to identifying trends. Multiple hierarchical clustering methods are available, e.g. meanshift, DBSCAN, Optics and HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) ( McInnes et al. , 2017 ). HDBSCAN is characterized by the least number of limitations. It takes each paper (vector) and checks at what distance it can find similar publications. Then it compares the results, and the densest areas are detected as clusters. Unlike some other methods, the clusters lack permanent density or a fixed number of elements. The only parameter that the researcher needs to establish is the minimum cluster size. The best value can be determined through a series of experiments.

In the present study, the authors carried out a set of experiments using different minimum cluster sizes. The highest value detected was 20. Lower values lead to a much higher number of clusters. Moreover, general phrases not directly related to HRM played a significant role in the discovery of these clusters. With the minimum cluster size set to values greater than 20, the number of clusters was significantly lower. That led to general results based on the most popular phrases only.

The entire sample was divided into groups of papers published in 5-year overlapping periods starting with 2000–2004 and ending with 2016–2020. Each paper was assigned to all the groups into which it fitted. Cluster analysis was performed for every group separately, and the results were used to identify trends.

Cluster analysis was performed on each group separately, and the results were used to discover trends. Approximately 30 clusters on average were identified for each five-year period. However, for a trend to be identified at least two similar clusters had to be discovered in successive periods Therefore, many unrelated clusters were excluded by the algorithm. Such behaviour is expected, as it removes noise from data. Usually, only one-third of clusters meet the conditions to form trends.

The number of papers published in each year is presented in Figure 2 . A slight decrease in the number of articles can be observed compared to 2018–19, which may have been a result of the Covid-19 pandemic.

3.4 Identification of trends

Long-lasting trends that existed and evolved during the studied period,

Declining trends which came to an end during the studied period,

Emerging trends which began during the studied period,

Ephemeris trends that began and ended during the studied period.

3.5 Interpretation of trends

The results delivered by the algorithm must be checked through further studies. The algorithm can detect mergers or splits in trends. We decided, however, that the final decision should be left to researchers. At this stage, trends should also be named, interpreted and described. The interpretation phase should help highlight changes within trends and try to predict their future evolution.

4. Results and discussion

The analyses, performed by researchers using automatic algorithms and further verification, led to the discovery of 42 trends presented in Table 3 . These trends are ordered according to the year of their first occurrence and their duration. It is worth emphasizing that the year in which a trend was observed does not indicate that the idea behind it emerged at the same time. Rather, it shows when a subject began to increase in popularity among researchers. Furthermore, the number of identified trends is much higher than the results from previous studies presented in Section 2.1 Table 4 .

The use of tracking revealed the evolution of clusters, and made it possible to identify trends. The analysis led to the discovery of the types of trends presented in Section 3.4. Of the 42 trends, 4 were long-lasting, 5 declining, 17 emerging and 16 ephemeris in character. One possible fact to note is that “strategic HRM”, which was a prevailing trend in previous studies, is not directly presented in the results obtained using text-mining analyses. However, it is included in the “architecture and changing role of HRM” trend.

At this point it is worth emphasizing that sociologists of science have examined the principles governing the selection of topics analysed by researchers, and noticed that it may result from a trade-off between conservative production and risky innovation ( Bourdieu, 1975 ). The main problem when choosing research topics is deciding whether to continue topics fixed in the literature or take the risk of exploring new, hitherto unknown themes. Trend a analysis offers an indirect solution based on strategic ambidexterity. This is not only because it allows us to observe disappearing themes that continue to be exploited, but also to identify those topics, in which there is a growing interest (exploration).

Long-lasting trends are not homogenous and change over time. The evolution of trends can be tracked using keywords of considerable importance in subsequent years. The importance of keywords was evaluated using the TF-IDF algorithm and averaged for each cluster. The TF-IDF formula was presented in the Methodology section. It should be noted that the TF-IDF score has to be calculated for each phrase in each paper. In this study, over 150,000 phrases were identified in over 6 thousand papers. That resulted in a significant number of calculations made by the algorithm, which cannot be presented in the paper. A comparison of cluster keywords reveals new topics within trends. The evolution of trends may lead to the disappearance of earlier topics or to their parallel development. Declining and ephemeris trends are associated with issues that are of less interest to researchers, have been resolved or were eclipsed by changes in a researcher's approach to the object of their study. The disappearance of certain trends is a normal phenomenon in science. Such a disappearance can be predicted to a certain degree when the average number of papers decreases.

Since we identified many trends, only a few examples will be described below. One example of a long-lasting trend is “Diversity Management”, which covered the entire 2000–2020 timeframe. The articles that discussed this trend focused on effective diversity management, its impact on organizational performance (e.g. Choi et al. , 2017 ), team performance ( Roberge and van Dick, 2010 ), knowledge sharing ( Shen et al. , 2014 ), innovation ( Peretz et al. , 2015 ), and the various factors which impact upon its effectiveness. Some papers discussed only one form of diversity in the workplace, e.g. age diversity ( Li et al. , 2011 ), gender diversity (e.g. Gould et al. , 2018 ) or ethnic diversity (e.g. Singh, 2007 ).

One sub-trend that can be observed within the above-discussed trend is age management', which falls within the 2005–2018 time range. The papers assigned to this sub-trend focus on HR practices towards older employees (e.g. Kooij et al. , 2014 ).

One example of a declining trend is “new and traditional career models”. This trend, which was observed in the years 2000–2019, highlights the fact that the weakening of organizational boundaries has increased career freedom and independence from previously constraining factors. The papers which examined this issue provide conceptual knowledge of different career dimensions. For example, a shift has taken place from objective to subjective careers. Individuals have to make sense of their careers, because they can no longer depend on their employers ( Walton and Mallon, 2004 ). Individual cultural, social and economic capital builds a field of opportunities for pursuing a career ( Iellatchitch et al. , 2003 ). Simultaneously, two major kinds of boundaries to the “boundaryless career” have been identified: the competence-based boundary (industry boundary) and the relation-based boundary (social capital boundary) ( Baghdadli et al. , 2003 ).

In the last two decades, increasing environmental awareness has pushed researchers towards addressing the issue of HRM as a strategic tool for making companies sustainability-driven organizations (e.g. Podgorodnichenko et al. , 2020 ). One of the emerging trends identified in our study is “Green and sustainable HRM”. This trend focuses on the environmental responsibility of companies (e.g. DuBois and Dubois, 2012 ) or/and achieving simultaneously social and economic goals (if the triple bottom line concept is discussed) (e.g. Ren and Jackson, 2020 ). The results, in the form of behavioral changes, have also been examined (e.g. Dumont et al. , 2017 ) and the contribution of HRM to company sustainability has been discussed in the context of different countries (e.g. Alcaraz et al. , 2019 ).

Finally, one example of an ephemeris trend is “HR certification”. The discussion on this trend was initiated by Lengnick-Hall and Aguinis (2012) . They applied a multi-level theory-based approach to investigating HR certification. They tried to assess the value of HR certification for individual HR specialists, their organizations as well as for the HR profession as a whole. The main topic addressed in later articles devoted to this trend was the value of HR certification (e.g. Aguinis and Lengnick-Hall, 2012 ). The value of HR certification has been linked with shareholder value ( Paxton, 2012 ). The link between organizational values and HR certification is another issue that has been addressed. Organizational values are treated as a key antecedent to the use and pursuit of HR certification ( Garza and Morgeson, 2012 ).

Table 3 presents only those periods during which specific trends were active, but provides no information on their dynamics. This can be observed by looking at the average number of papers per year (ANPY) in consecutive periods. Table 4 presents all the trends active during the last year of the study. They were divided into three groups according to whether the ANPY was decreasing, increasing or stable in recent years. To depict the relative strength of these trends, table shows the average number of papers published in the final 5-year period.

It can be concluded that trends with an increasing dynamic coincide with the trends defined in the literature. For example, “flexible employment from the perspective of HRM” corresponds with “employment relations” distinguished by Markoulli et al. (2017) and “the HRM process, the changing nature of HRM, and precarious employment relations” in the typology developed by Cooper et al. (2020) . “Diversity Management” is related to “organizational culture” ( Özlen, 2014 ). “Employee participation” may be associated with “employment relations” ( Cooke et al. , 2019 ) and “organizational commitment” ( García-Lillo et al. , 2017 ). The latter occurs both in the presented typology and in previous ones. “leader–member exchange” should be included in “behavioral issues” ( Özlen, 2014 ). Finally, a trend characterized by an increasing dynamic is “green and sustainable HRM”. Green HRM was an independent subject of analysis in a study by Yong et al. (2020) .

5. Conclusions

5.1 contributions and implications.

The present study provides an analysis of HRM journals with the aim of identifying trends in HRM research. It makes contributions to the field by providing a more comprehensive and objective review than analyses resulting from conventional systematic literature reviews as well as by identifying 42 different trends. It fills an existing gap in literature studies on HRM with a novel research approach – a methodology based on full-text mining and a big data toolset. As a consequence, this study can be considered as providing an adequate reflection of all the articles published in journals strictly devoted to HRM issues and which may serve as an important source of reference for both researchers and practitioners. It can also help them identify the core journals focused on HRM research as well as those topics which are of particular interest and importance.

As the study covers a period of over 20 years it should come as no surprise that some trends emerged and declined over this time. However, our study creates an opportunity for reviving research topics which combine old trends with new ones, and at the same time take into account the interdisciplinary nature of HRM as a field of research. Some researchers have observed that success can often be achieved by adopting a tool from another research area or through a new way of analyzing old problems that brings new insights and solutions ( Adali et al. , 2018 ).

Finally, we observed the emergence of a number of trends during the studied period that are still active. In particular, green and sustainable HRM is not only an emerging trend but also developing rapidly. It is worth mentioning here that while many articles have focused on green HRM issues, they have not been published in journals that specialize in HRM but in journals devoted to environmental issues. One possible future challenge for researchers may be to estimate the proportions between HRM articles published in HRM journals and those featured in other journals.

Practitioners interested in the evolution of the field can find in this paper areas of HRM that require improving in their own businesses or which can be treated as a platform for introducing innovations in HRM (emerging trends). The information contained in this paper can also be utilized as a source for evaluating the performance of sub-fields in a HRM research domain and for adjusting research policies with regard to funding allocations and comparing research input and output ( Gu, 2004 ). The editors of journals may take into account the results presented in this paper when making decisions regarding the direction, scope, and themes of their journals.

5.2 Limitations

First, our eligibility criteria included only papers indexed in the Scopus and WoS database and excluded conference proceedings, book chapters, and non-English papers. Second, only full-text articles were included in the study, which could narrow down the research area. As a consequence, important information regarding the research presented in the excluded documents is potentially lost. Third, most of the papers in our database were published in the International Journal of Human Resource Management, and therefore such trends as “challenges for international HRM” can be considered significant (long-lasting). Another – the fourth – limitation of the study is the lack of estimation of the proportion between searches in HRM journals and articles published in other journals. Future research may overcome the above-presented limitations. Although we used valuable techniques such as TF-IDF and HDBSCAN, the fifth limitation is that, after trends were discovered, it was necessary to evaluate and interpret them. That could have induced researchers' bias even if – as in this study – researchers from different areas of experience were involved. Finally, this study covers the 2000–2020 timeframe. Since HRM is a rapidly developing field, in a few years from now academics will probably begin to move into exciting new research areas. As a consequence, it might be worthwhile conducting similar analyses to those presented in this study and compare their results.

Workflow of the methodology used in this study

Number of papers in the years 2000–2020

Trends in HRM research identified in previous studies

HRM-related journals included in this study

Trends in HRM research in the years 2000–2020

Activity of long-lasting and emerging trends in recent years

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Title: researchagent: iterative research idea generation over scientific literature with large language models.

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Published on 17.4.2024 in Vol 26 (2024)

Service Quality and Residents’ Preferences for Facilitated Self-Service Fundus Disease Screening: Cross-Sectional Study

Authors of this article:

Original Paper

Senlin Lin 1, 2, 3 * , MSc ;
Yingyan Ma 1, 2, 3, 4 * , PhD ;
Yanwei Jiang 5 * , MPH ;
Wenwen Li 6 , PhD ;
Yajun Peng 1, 2, 3 , BA ;
Tao Yu 1, 2, 3 , BA ;
Yi Xu 1, 2, 3 , MD ;
Jianfeng Zhu 1, 2, 3 , MD ;
Lina Lu 1, 2, 3 , MPH ;
Haidong Zou 1, 2, 3, 4 , MD

1 Shanghai Eye Diseases Prevention &Treatment Center/ Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China

2 National Clinical Research Center for Eye Diseases, Shanghai, China

3 Shanghai Engineering Research Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China

4 Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

5 Shanghai Hongkou Center for Disease Control and Prevention, Shanghai, China

6 School of Management, Fudan University, Shanghai, China

*these authors contributed equally

Corresponding Author:

Haidong Zou, MD

Shanghai Eye Diseases Prevention &Treatment Center/ Shanghai Eye Hospital

School of Medicine

Tongji University

No 1440, Hongqqiao Road

Shanghai, 200336

Phone: 86 02162539696

Email: [email protected]

Background: Fundus photography is the most important examination in eye disease screening. A facilitated self-service eye screening pattern based on the fully automatic fundus camera was developed in 2022 in Shanghai, China; it may help solve the problem of insufficient human resources in primary health care institutions. However, the service quality and residents’ preference for this new pattern are unclear.

Objective: This study aimed to compare the service quality and residents’ preferences between facilitated self-service eye screening and traditional manual screening and to explore the relationships between the screening service’s quality and residents’ preferences.

Methods: We conducted a cross-sectional study in Shanghai, China. Residents who underwent facilitated self-service fundus disease screening at one of the screening sites were assigned to the exposure group; those who were screened with a traditional fundus camera operated by an optometrist at an adjacent site comprised the control group. The primary outcome was the screening service quality, including effectiveness (image quality and screening efficiency), physiological discomfort, safety, convenience, and trustworthiness. The secondary outcome was the participants’ preferences. Differences in service quality and the participants’ preferences between the 2 groups were compared using chi-square tests separately. Subgroup analyses for exploring the relationships between the screening service’s quality and residents’ preference were conducted using generalized logit models.

Results: A total of 358 residents enrolled; among them, 176 (49.16%) were included in the exposure group and the remaining 182 (50.84%) in the control group. Residents’ basic characteristics were balanced between the 2 groups. There was no significant difference in service quality between the 2 groups (image quality pass rate: P =.79; average screening time: P =.57; no physiological discomfort rate: P =.92; safety rate: P =.78; convenience rate: P =.95; trustworthiness rate: P =.20). However, the proportion of participants who were willing to use the same technology for their next screening was significantly lower in the exposure group than in the control group ( P <.001). Subgroup analyses suggest that distrust in the facilitated self-service eye screening might increase the probability of refusal to undergo screening ( P =.02).

Conclusions: This study confirms that the facilitated self-service fundus disease screening pattern could achieve good service quality. However, it was difficult to reverse residents’ preferences for manual screening in a short period, especially when the original manual service was already excellent. Therefore, the digital transformation of health care must be cautious. We suggest that attention be paid to the residents’ individual needs. More efficient man-machine collaboration and personalized health management solutions based on large language models are both needed.

Introduction

Vision impairment and blindness are caused by a variety of eye diseases, including cataracts, glaucoma, uncorrected refractive error, age-related macular degeneration, diabetic retinopathy, and other eye diseases [ 1 ]. They not only reduce economic productivity but also harm the quality of life and increase mortality [ 2 - 6 ]. In 2020, an estimated 43.3 million individuals were blind, and 1.06 billion individuals aged 50 years and older had distance or near vision impairment [ 7 ]. With an increase in the aging population, the number of individuals affected by vision loss has increased substantially [ 1 ].

High-quality public health care for eye disease prevention, such as effective screening, can assist in eliminating approximately 57% of all blindness cases [ 8 ]. Digital technologies, such as telemedicine, 5G telecommunications, the Internet of Things, and artificial intelligence (AI), have provided the potential to improve the accessibility, availability, and productivity of existing resources and the overall efficiency of eye care services [ 9 , 10 ]. The use of digital technology not only reduces the cost of eye disease screening and improves its efficiency, but also assists residents living in remote areas to gain access to eye disease screening [ 11 - 13 ]. Therefore, an increasing number of countries (or regions) are attempting to establish eye screening systems based on digital technology [ 9 ].

Fundus photography is the most important examination in eye disease screening because the vast majority of diagnoses of blinding retinal diseases are based on fundus photographs. Diagnoses can be made by human experts or AI software. However, traditional fundus cameras must be operated by optometrists, who are usually in short supply in primary health care institutions when faced with the large demand for screening services.

Fortunately, the fully automatic fundus camera has been developed on the basis of digital technologies including AI, industrial automation, sensors, and voice navigation. It can automatically identify the person’s left and right eyes, search for pupils, adjust the lens position and shooting focus, and provide real-time voice feedback during the process, helping the residents to understand the current inspection steps clearly and cooperatively complete the inspection. Therefore, a facilitated self-service eye screening pattern has been newly established in 2022 in Shanghai, China.

However, evidence is inadequate about whether this new screening pattern performs well and whether the residents prefer it. Therefore, this cross-sectional study aims to compare the service quality and residents’ preferences of this new screening pattern with that of the traditional screening pattern. We aimed to (1) investigate whether the facilitated self-service eye screening can achieve service quality similar to that of traditional manual screening, (2) compare residents’ preferences between the facilitated self-service eye screening and traditional manual screening, and (3) explore the relationship between the screening service quality and residents’ preferences.

Study Setting

This study was conducted in Shanghai, China, in 2022. Since 2010, Shanghai has conducted an active community-based fundus disease telemedicine screening program. After 2018, an AI model was adopted ( Figure 1 ). At the end of 2021, the fully automatic fundus camera was adopted, and the facilitated self-service fundus disease screening pattern was established ( Figure 1 ). Within this new pattern, residents could perform fundus photography by themselves without professionals’ assistance ( Multimedia Appendix 1 ). The fundus images were sent to the cloud server center of the AI model, and the screening results were fed back immediately.

Study Design

We conducted a cross-sectional study at 2 adjacent screening sites. These 2 sites were expected to be very similar in terms of their socioeconomic and educational aspects since they were located next to each other. One site provided facilitated self-service fundus disease screening, and the residents who participated therein comprised the exposure group; the other site provided screening with a traditional fundus camera operated by an optometrist, and the residents who participated therein comprised the control group. All the adult residents could participant in our screening program, but their data were used for analysis only if they signed the informed consent form. Residents could opt out of the study at any time during the screening.

In the exposure group, the residents were assessed using an updated version of the nonmydriatic fundus camera Kestrel 3100m (Shanghai Top View Industrial Co Ltd) with a self-service module. In the process of fundus photography, the residents pressed the “Start” button by themselves. All checking steps (including focusing, shooting, and image quality review) were undertaken automatically by the fundus camera ( Figure 2 ). Screening data were transmitted to the AI algorithm on a cloud-based server center through the telemedicine platform, and the screening results were fed back immediately. Residents were fully informed that the assessment was fully automated and not performed by the optometrist.

In the control group, the residents were assessed using the basic version of the same nonmydriatic fundus camera. The optical components were identical to those in the exposure group but without the self-service module. In the process of fundus photography, all steps were carried out by the optometrist (including focusing, shooting, and image quality review). Screening data were transmitted to the AI algorithm on a cloud-based server center through the telemedicine platform, and the screening results were fed back immediately. Residents were also fully informed.

Measures and Outcomes

The primary outcome was the screening service’s quality. Based on the World Health Organization’s recommendations for the evaluation of AI-based medical devices [ 14 ] and the European Union’s Assessment List for Trustworthy Artificial Intelligence [ 15 ], 5 dimensions were selected to reflect the service quality of eye disease screening: effectiveness, physiological discomfort, safety, convenience, and trustworthiness.

Furthermore, effectiveness was based on 2 indicators: image quality and screening efficiency. A staff member recorded the time required for each resident to take fundus photographs (excluding the time taken for diagnosis) at the screening site. Then, a professional ophthalmologist evaluated the quality of each fundus photograph after the on-site experiment. The ophthalmologist was blinded to the grouping of participants. Image quality was assessed on the basis of the image quality pass rate, expressed as the number of eyes with high-quality fundus images per 100 eyes. Screening efficiency was assessed on the basis of the average screening time, expressed as the mean of the time required for each resident to take fundus photographs.

To assess physiological discomfort, safety, convenience, and trustworthiness of screening services, residents were asked to finish a questionnaire just after they received the screening results. A 5-point Likert scale was adopted for each dimension, from the best to the worst, except for the physiological discomfort ( Multimedia Appendix 2 ). A no physiological discomfort rate was expressed as the number of residents who chose the “There is no physiological discomfort during the screening” per 100 individuals in each group. Safety rate is expressed as the number of residents who chose “The screening is very safe” or “The screening is safe” per 100 individuals in each group. Convenience rate is expressed as the number of residents who chose “The screening is very convenient” or “The screening is convenient” per 100 individuals in each group. The trustworthiness rate is expressed as the number of residents who chose “The screening result is very trustworthy” or “The screening result is trustworthy” per 100 individuals in each group.

The secondary outcome was the preference rate, expressed as the number of residents who were willing to use the same technology for their next screening per 100 individuals. In detail, in the exposure group, the preference rate was expressed as the number of the residents who preferred facilitated self-service eye screening per 100 individuals, while in the control group, it was expressed as the number of residents who preferred traditional manual screening per 100 individuals.

To understand the residents’ preference, a video displaying the processes of both facilitated self-service eye screening and traditional manual screening was shown to the residents. Then, the following question was asked: “At your next eye disease screening, you can choose either facilitated self-service eye screening or traditional manual screening. Which one do you prefer?” A total of 4 alternatives were set: “Prefer traditional manual screening,” “Prefer facilitated self-service eye screening,” “Both are acceptable,” and “Neither is acceptable (Refusal of screening).” Each resident could choose only 1 option, which best reflected their preference.

Sample Size

The rule of events per variable was used for sample size estimation. In this study, 2 logit models were established for the 2 groups separately, each containing 8 independent variables. We set 10 events per variable in general. According to a previous study [ 16 ], when the decision-making process had high uncertainty, the proportion of individuals who preferred the algorithms was about 50%. This led us to arrive at a sample size of 160 (8 variables multiplied by 10 events each, with 50% of individuals potentially preferring facilitated screening [ie, 50% of 8×10]) for each group.

Every dimension of the screening service quality and the preference rate were calculated separately. Chi-square and t tests were used to test whether the service quality or the residents’ preferences differed between the 2 groups. A total of 7 hypotheses were tested, as shown in Textbox 1 .

H1: image quality pass rate exposure group ≠ image quality pass rate control group H0: image quality pass rate exposure group =image quality pass rate control group
H1: screening time exposure group ≠screening time control group H0: screening time exposure group =screening time control group
H1: no discomfort rate exposure group ≠no discomfort rate control group H0: no discomfort rate exposure group = no discomfort rate control group
H1: safety rate exposure group ≠safety rate control group H0: safety rate exposure group = safety rate control group
H1: convenience rate exposure group ≠convenience rate control group H0: convenience rate exposure group = convenience rate control group
H1: trustworthiness rate exposure group ≠trustworthiness rate control group H0: trustworthiness rate exposure group = trustworthiness rate control group
H1: preference rate exposure group ≠preference rate control group H0: preference rate exposure group = preference rate control group

If any of the hypotheses among hypotheses 1-6 ( Textbox 1 ) were significant, it indicated that the service quality was different between facilitated self-service eye screening and traditional manual screening. If hypothesis 7 was significant, it meant that the residents’ preference for facilitated self-service eye screening was different from that for traditional manual screening.

Additionally, subgroup analyses in the exposure and control groups were conducted to explore the relationships between the screening service quality and the residents’ preferences, using generalized logit models. The option “Prefer facilitated self-service eye screening” was used as the reference level for the dependent variable in the models. The independent variables included age, sex, image quality, screening efficiency, physiological discomfort, safety, convenience, and trustworthiness. All statistics were performed using SAS (version 9.4; SAS Institute).

Ethical Considerations

The study adhered to the ethical principles of the Declaration of Helsinki and was approved by the Shanghai General Hospital Ethics Committee (2022SQ272). All participants provided written informed consent before participating in this study. The study data were anonymous, and no identification of individual participants in any images of the manuscript or supplementary material is possible.

Participants’ Characteristics

A total of 358 residents enrolled; among them, 176 (49.16%) were in the exposure group and the remaining 182 (50.84%) were in the control group. Residents’ basic characteristics were balanced between the 2 groups. The mean age was 65.05 (SD 12.28) years for the exposure group and 63.96 (SD 13.06) years for the control group; however, this difference was nonsignificant ( P =.42). The proportion of women was 67.05% (n=118) for the exposure group and 62.09% (n=113) for the control group; this difference was also nonsignificant between the 2 groups ( P =.33).

Screening Service Quality

In the exposure group, high-quality fundus images were obtained for 268 out of 352 eyes (image quality pass rate=76.14%; Figure 3 ). The average screening time was 81.03 (SD 36.98) seconds ( Figure 3 ). In the control group, high-quality fundus images were obtained for 274 out of 364 eyes (image quality pass rate=75.27%; Figure 3 ). The average screening time was 78.22 (SD 54.01) seconds ( Figure 3 ). There was no significant difference in the image quality pass rate ( χ 2 1 =0.07, P =.79) and average screening time ( t 321.01 =–0.58 [Welch–Satterthwaite–adjusted df ], P =.56) between the 2 groups ( Figure 3 ).

For the other dimensions, detailed information is shown in Figure 3 . There were no significant differences between any of these rates between the 2 groups (no physiological discomfort rate: χ 2 1 =0.01, P =.92; safety rate: χ 2 1 =0.08, P =.78; convenience rate: χ 2 1 =0.004, P =.95; trustworthiness rate: χ 2 1 =1.63, P =.20).

Residents’ Preferences

In the exposure group, 120 (68.18%) residents preferred traditional manual screening, 19 (10.80%) preferred facilitated self-service eye screening, 19 (10.80%) preferred both, and the remaining 18 (10.23%) preferred neither. In the control group, 123 (67.58%) residents preferred traditional manual screening, 14 (7.69%) preferred facilitated self-service eye screening, 20 (10.99%) preferred both, and the remaining 25 (13.74%) preferred neither.

The proportion of residents who chose the category “Prefer facilitated self-service eye screening” in the exposure group was significantly lower than that of residents who chose the category “Prefer traditional manual screening” in the control group ( χ 2 1 =120.57, P <.001; Figure 3 ).

Subgroup Analyses

In the exposure group, 4 generalized logit models were generated ( Table 1 ). Regarding the effectiveness of facilitated self-service eye screening, neither the image quality nor the screening time had an impact on the residents’ preferences. Regarding the other dimensions for facilitated self-service eye screening service quality, models 3 and 4 demonstrated that distrust in the results of facilitated self-service eye screening might decrease the probability of preferring this screening service and increase the probability of preferring neither of the 2 screening services.

a Age and gender were adjusted in model 1. Age, gender, image quality, and screening efficiency were adjusted in model 2. Age, gender, physiological discomfort, safety, convenience, and trustworthiness were adjusted in model 3. Age, gender, image quality, screening efficiency, physiological discomfort, safety, convenience, and trustworthiness were adjusted in model 4.

b In the exposure group, distrust in the results of facilitated self-service eye screening might decrease the probability of preferring this screening service and increase the probability of preferring neither the traditional nor the facilitated self-service screening services.

c Not available.

In the control group, another 4 generalized logit models were generated ( Table 2 ). Men were more likely to choose a preference both screening services. The probability of preferring manual screening might increase with age, as long as the probability of preferring facilitated self-service eye screening decreased. Regarding the effectiveness of traditional manual screening, neither the image quality pass rate nor the screening time had an impact on the residents’ preferences. For the other dimensions of the quality of traditional manual screening, models 7 and 8 showed that if the residents feel unsafe about traditional manual screening, their preference for traditional manual screening might decrease, and they might turn to facilitated self-service eye screening.

a Age and gender were adjusted in model 5. Age, gender, image quality, and screening efficiency were adjusted in model 6. Age, gender, physiological discomfort, safety, convenience, and trustworthiness were adjusted in model 7. Age, gender, image quality, screening efficiency, physiological discomfort, safety, convenience, and trustworthiness were adjusted in model 8.

b In the control group, if the residents feel unsafe about traditional manual screening, their preference for traditional manual screening might decrease, and they might turn to facilitated self-service eye screening.

A new fundus disease screening pattern was established using the fully automatic fundus camera without any manual intervention. Our findings suggest that facilitated self-service eye screening can achieve a service quality similar to that of traditional manual screening. The study further evaluated the residents’ preferences and associated factors for the newly established self-service fundus disease screening. Our study found that the residents’ preference for facilitated self-service eye screening is significantly less than that for traditional manual screening. This implies that the association between the service quality of the screening technology and residents’ preferences was weak, suggesting that aversion to the algorithm might exist. In addition, the subgroup analyses suggest that even the high quality of facilitated self-service eye screening cannot increase the residents’ preference for this new screening pattern. Worse still, distrust in the results of this new pattern may lead to lower usage of eye disease screening services as a whole. To the best of our knowledge, this study is one of the first to evaluate service quality and residents’ preferences for facilitated self-service fundus disease screening.

Previous studies have suggested that people significantly prefer manual services to algorithms in the field of medicine [ 16 - 18 ]. Individuals have an aversion to algorithms underlying digital technology, especially when they see errors in the algorithm’s functioning [ 18 ]. The preference for algorithms does not increase even if the residents are told that the algorithm outperforms human doctors [ 19 , 20 ]. Our results confirm that fundus image quality in the exposure group is similar to that in the control group in our study, and both are similar to or even better than those reported in previous studies [ 21 , 22 ]. However, the preference for facilitated self-service fundus disease screening is significantly less than that for traditional manual screening. One possible explanation is that uniqueness neglect—a concern that algorithm providers are less able than human providers to account for residents’ (or patients’) unique characteristics and circumstances—drives consumer resistance to digital medical technology [ 23 ]. Therefore, personalized health management solutions based on large language models should be developed urgently [ 24 ] to meet the residents’ individual demands. In addition, a survey of population preferences for medical AI indicated that the most important factor for the public is that physicians are ultimately responsible for diagnosis and treatment planning [ 25 ]. As a result, man-machine collaboration, such as human supervision, is still necessary [ 26 ], especially in the early stages of digital transformation to help residents understand and accept the digital technologies.

Furthermore, our study suggests that distrust in the results of facilitated self-service fundus disease screening may cause residents to abandon eye disease screening, irrespective of whether it is provided using this new screening pattern or via the traditional manual screening pattern. This is critical to digital transformation in medicine. This implies that if the digital technology does not perform well, residents will not only be averse to the digital technology itself but also be more likely to abandon health care services as a whole. Digital transformation is a fundamental change to the health care delivery system. This implies that it can self-disrupt its ability to question the practices and production models of existing health care services. As a result, it may become incompatible with the existing models, processes, activities, and even cultures [ 27 ]. Therefore, it is important to assess whether the adoption of digital technologies contributes to health system objectives in an optimal manner, and this assessment should be carried out at the level of health services but not at the level of digital transformation [ 28 ].

The most prominent limitation of our study is that it was conducted only in Shanghai, China. Because of the sound health care system in Shanghai, residents have already received high-quality eye disease screening services before the adoption of the facilitated self-service eye screening pattern. Consequently, residents are bound to demand more from this new pattern. This situation is quite different from that in lower-income regions. Digital technology was adapted in poverty-stricken areas to build an eye care system, but it did not replace the original system that is based on manually delivered services [ 13 ]. Therefore, the framing effect may be weak [ 29 ], and there is little practical value in comparing digital technology and manual services in these regions. Second, our study is an observational study and blind grouping was not practical due to the special characteristics of fundus examination. However, we have attempted to use blind processing whenever possible. For instance, ophthalmologists’ evaluation of image quality was conducted in a blinded manner. Third, the manner in which we inquired about residents’ preferences might affect the results. For example, participants in the exposure group generally have experience with manual screening, but those in the control group may not have had enough experience with facilitated screening despite having been shown a video. This might make the participants in the control group more likely to choose manual screening because the new technology was unfamiliar. Finally, individual-level socioeconomic factors or educational level were not recorded, so we cannot rule out the influence of these factors on residents’ preferences.

In summary, this study confirms that the facilitated self-service fundus disease screening pattern could achieve high service quality. The preference of the residents for this new mode, however, was not ideal. It was difficult to reverse residents’ preference for manual screening in a short period, especially when the original manual service was already excellent. Therefore, the digital transformation of health care must proceed with caution. We suggest that attention be paid to the residents’ individual needs. Although more efficient man-machine collaboration is necessary to help the public understand and accept new technologies, personalized health management solutions based on large language models are required.

Acknowledgments

This study was funded by the Shanghai Public Health Three-Year Action Plan (GWVI-11.1-30, GWVI-11.1-22), Science and Technology Commission of Shanghai Municipality (20DZ1100200 and 23ZR1481000), Shanghai Municipal Health Commission (2022HP61, 2022YQ051, and 20234Y0062), Shanghai First People's Hospital featured research projects (CCTR-2022C08) and Medical Research Program of Hongkou District Health Commission (Hongwei2202-07).

Data Availability

Data are available from the corresponding author upon reasonable request.

Authors' Contributions

SL, YM, and YJ contributed to the conceptualization and design of the study. SL, YM, YJ, YP, TY, and YX collected the data. SL and YM analyzed the data. SL, YM, and YJ drafted the manuscript. WL, YX, JZ, LL, and HZ extensively revised the manuscript. All authors read and approved the final manuscript submitted.

Conflicts of Interest

None declared.

Video of the non-mydriatic fundus camera Kestrel-3100m with the self-service module.

Questions for screening service quality.

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Abbreviations

Edited by A Mavragani; submitted 06.01.23; peer-reviewed by B Li, A Bate, CW Pan; comments to author 13.09.23; revised version received 15.10.23; accepted 12.03.24; published 17.04.24.

©Senlin Lin, Yingyan Ma, Yanwei Jiang, Wenwen Li, Yajun Peng, Tao Yu, Yi Xu, Jianfeng Zhu, Lina Lu, Haidong Zou. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 17.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

Open access
Published: 17 April 2024

A data-driven combined prediction method for the demand for intensive care unit healthcare resources in public health emergencies

Weiwei Zhang 1 &
Xinchun Li 1

BMC Health Services Research volume 24 , Article number: 477 ( 2024 ) Cite this article

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Public health emergencies are characterized by uncertainty, rapid transmission, a large number of cases, a high rate of critical illness, and a high case fatality rate. The intensive care unit (ICU) is the “last line of defense” for saving lives. And ICU resources play a critical role in the treatment of critical illness and combating public health emergencies.

This study estimates the demand for ICU healthcare resources based on an accurate prediction of the surge in the number of critically ill patients in the short term. The aim is to provide hospitals with a basis for scientific decision-making, to improve rescue efficiency, and to avoid excessive costs due to overly large resource reserves.

A demand forecasting method for ICU healthcare resources is proposed based on the number of current confirmed cases. The number of current confirmed cases is estimated using a bilateral long-short-term memory and genetic algorithm support vector regression (BILSTM-GASVR) combined prediction model. Based on this, this paper constructs demand forecasting models for ICU healthcare workers and healthcare material resources to more accurately understand the patterns of changes in the demand for ICU healthcare resources and more precisely meet the treatment needs of critically ill patients.

Data on the number of COVID-19-infected cases in Shanghai between January 20, 2020, and September 24, 2022, is used to perform a numerical example analysis. Compared to individual prediction models (GASVR, LSTM, BILSTM and Informer), the combined prediction model BILSTM-GASVR produced results that are closer to the real values. The demand forecasting results for ICU healthcare resources showed that the first (ICU human resources) and third (medical equipment resources) categories did not require replenishment during the early stages but experienced a lag in replenishment when shortages occurred during the peak period. The second category (drug resources) is consumed rapidly in the early stages and required earlier replenishment, but replenishment is timelier compared to the first and third categories. However, replenishment is needed throughout the course of the epidemic.

The first category of resources (human resources) requires long-term planning and the deployment of emergency expansion measures. The second category of resources (drugs) is suitable for the combination of dynamic physical reserves in healthcare institutions with the production capacity reserves of corporations. The third category of resources (medical equipment) is more dependent on the physical reserves in healthcare institutions, but care must be taken to strike a balance between normalcy and emergencies.

Peer Review reports

Introduction

The outbreak of severe acute respiratory syndrome (SARS) in 2003 was the first global public health emergency of the 21st century. From SARS to the coronavirus disease (COVID-19) pandemic at the end of 2019, followed shortly by the monkeypox epidemic of 2022, the global community has witnessed eight major public health events within the span of only 20 years [ 1 ]. These events are all characterized by high infection and fatality rates. For example, the number of confirmed COVID-19 cases worldwide is over 700 million, and the number of deaths has exceeded 7 million [ 2 ]. Every major public health emergency typically consists of four stages: incubation, outbreak, peak, and decline. During the outbreak and transmission, surges in the number of infected individuals and the number of critically ill patients led to a corresponding increase in the urgent demand for intensive care unit (ICU) medical resources. ICU healthcare resources provide material security for rescue work during major public health events as they allow critically ill patients to be treated, which decreases the case fatality rate and facilitates the prevention and control of epidemics. Nevertheless, in actual cases of prevention and control, the surge in patients has often led to shortages of ICU healthcare resources and a short-term mismatch of supply and demand, which are problems that have occurred several times in different regions. These issues can drastically impact anti-epidemic frontline healthcare workers and the treatment outcomes of infected patients. According to COVID-19 data from recent years, many infected individuals take about two weeks to progress from mild to severe disease. As the peak of severe cases tends to lag behind that of infected cases, predicting the changes in the number of new infections can serve as a valuable reference for healthcare institutions in forecasting the demand for ICU healthcare resources. The accurate forecasting of the demand for ICU healthcare resources can facilitate the rational resource allocation of hospitals under changes in demand patterns, which is crucial for improving the provision of critical care and rescue efficiency. Therefore, in this study, we combined a support vector regression (SVR) prediction model optimized by a genetic algorithm (GA) with bidirectional long-short-term memory (BILSTM), with the aim of enhancing the dynamic and accurate prediction of the number of current confirmed cases. Based on this, we forecasted the demand for ICU healthcare resources, which in turn may enable more efficient resource deployment during severe epidemic outbreaks and improve the precise supply of ICU healthcare resources.

Research on the demand forecasting of emergency materials generally employs quantitative methods, and traditional approaches mainly include linear regression and GM (1,1). Linear regression involves the use of regression equations to make predictions based on data. Sui et al. proposed a method based on multiple regression that aimed to predict the demand for emergency supplies in the power grid system following natural disasters [ 3 ]. Historical data was used to obtain the impact coefficient of each factor on emergency resource forecasting, enabling the quick calculation of the demand for each emergency resource during a given type of disaster. However, to ensure prediction accuracy, regression analysis needs to be supported by data from a large sample size. Other researchers have carried out demand forecasting for emergency supplies from the perspective of grey prediction models. Li et al. calculated the development coefficient and grey action of the grey GM (1,1) model using the particle swarm optimization algorithm to minimize the relative errors between the real and predicted values [ 4 ]. Although these studies have improved the prediction accuracy of grey models, they mainly involve pre-processing the initial data series without considering the issue of the excessively fast increase in predicted values by traditional grey GM (1,1) models. In emergency situations, the excessively fast increase in predicted values compared to real values will result in the consumption of a large number of unnecessary resources, thereby decreasing efficiency and increasing costs. As traditional demand forecasting models for emergency supplies have relatively poor perfect order rates in demand analysis, which result in low prediction accuracy, they are not mainstream.

At present, dynamic models of infectious diseases and demand forecasting models based on machine learning are at the cutting edge of research. With regard to the dynamic models of infectious diseases, susceptible infected recovered model (SIR) is a classic mathematical model employed by researchers [ 5 , 6 , 7 ]. After many years of development, the SIR model has been expanded into various forms within the field of disease transmission, including susceptible exposed infected recovered model (SEIR) and susceptible exposed infected recovered dead model (SEIRD) [ 8 , 9 ]. Nevertheless, with the outbreak of COVID-19, dynamic models of infectious diseases have once again come under the spotlight, with researchers combining individual and group variables and accounting for different factors to improve the initial models and reflect the state of COVID-19 [ 10 , 11 , 12 , 13 ]. Based on the first round of epidemic data from Wuhan, Li et al. predicted the time-delay distributions, epidemic doubling time, and basic reproductive number [ 14 ]. Upon discovering the presence of asymptomatic COVID-19 infections, researchers began constructing different SEIR models that considered the infectivity of various viral incubation periods, yielding their respective predictions of the inflection point. Based on this, Anggriani et al. further considered the impact of the status of infected individuals and established a transmission model with seven compartments [ 15 ]. Efimov et al. set the model parameters for separating the recovered and the dead as uncertain and applied the improved SEIR model to analyze the transmission trend of the pandemic [ 16 ]. In addition to analyzing the transmission characteristics of normal COVID-19 infection to predict the status of the epidemic, many researchers have also used infectious disease models to evaluate the effects of various epidemic preventive measures. Lin et al. applied an SEIR model that considered individual behavioral responses, government restrictions on public gatherings, pet-related transmission, and short-term population movements [ 17 ]. Cao et al. considered the containment effect of isolation measures on the pandemic and solved the model using Euler’s numerical method [ 18 ]. Reiner et al. employed an improved SEIR model to study the impact of non-pharmaceutical interventions implemented by the government (e.g., restricting population movement, enhancing disease testing, and increasing mask use) on disease transmission and evaluated the effectiveness of social distancing and the closure of public spaces [ 19 ]. These studies have mainly focused on modeling the COVID-19 pandemic to perform dynamic forecasting and analyze the effectiveness of control measures during the epidemic. Infectious disease dynamics offer good predictions for the early transmission trends of epidemics. However, this approach is unable to accurately estimate the spread of the virus in open-flow environments. Furthermore, it is also impossible to set hypothetical parameters, such as disease transmissibility and the recovery probability constant, that are consistent with the conditions in reality. Hence, with the increase in COVID-19 data, this approach has become inadequate for the accurate long-term analysis of epidemic trends.

Machine learning has shown significant advantages in this regard [ 20 , 21 ]. Some researchers have adopted the classic case-based reasoning approach in machine learning to make predictions. However, it is not feasible to find historical cases that fully match the current emergency event, so this approach has limited operability. Other researchers have also employed neural network training in machine learning to make predictions. For example, Hamou et al. predicted the number of injuries and deaths, which in turn were used to forecast the demand for emergency supplies [ 22 ]. However, this approach requires a large initial dataset and a high number of training epochs, while uncertainty due to large changes in intelligence information can lead to significant errors in data prediction [ 23 , 24 , 25 ]. To address these problems, researchers have conducted investigations that account (to varying degrees) for data characterized by time-series and non-linearity and have employed time-series models with good non-linear fitting [ 26 , 27 , 28 ]. The use of LSTM to explore relationships within the data can improve the accuracy of predicting COVID-19 to some extent. However, there are two problems with this approach. First, LSTM neural networks require extremely large datasets, and each wave of the epidemic development cycle would be insufficient to support a dataset suitable for LSTM. Second, neural networks involve a large number of parameters and highly complex models and, hence, are susceptible to overfitting, which can prevent them from achieving their true and expected advantages in prediction.

Overall, Our study differs from other papers in the following three ways. First, the research object of this paper focuses on the specific point of ICU healthcare resource demand prediction, aiming to improve the rate of critical care patient treatment. However, past research on public health emergencies has focused more on resource prediction , such as N95 masks, vaccines, and generalized medical supplies during the epidemic , to mitigate the impact of rapid transmission and high morbidity rates. This has led to less attention being paid to the reality of the surge in critically ill patients due to their high rates of severe illness and mortality.

Second, the idea of this paper is to further forecast resource needs based on the projected number of people with confirmed diagnoses, which is more applicable to healthcare organizations than most other papers that only predict the number of people involved. However, in terms of the methodology for projecting the number of people, this paper adopts a combined prediction method that combines regression algorithms and recurrent neural networks to propose a BILSTM-GASVR prediction model for the number of confirmed diagnoses. It capitalizes on both the suitability of SVR for small samples and non-linear prediction as well as the learning and memory abilities of BILSTM in processing time-series data. On the basis of the prediction model for the number of infected cases, by considering the characteristics of ICU healthcare resources, we constructed a demand forecasting model of emergency healthcare supplies. Past public health emergencies are more likely to use infectious disease models or a single prediction model in deep learning. some of the articles, although using a combination of prediction, but also more for the same method domain combination, such as CNN-LSTM, GRU-LSTM, etc., which are all recurrent neural networks.

Third, in terms of specific categorization of resources to be forecasted, considering the specificity of ICU medical resources, we introduce human resource prediction on the basis of previous studies focusing on material security, and classified ICU medical resources into three categories: ICU human resources, drugs and medical equipment. The purpose of this classification is to match the real-life prediction scenarios of public health emergencies and improve the demand forecasting performance for local ICU healthcare resources. Thus, it is easy for healthcare institutions to grasp the overall development of events, optimizing decision-making, and reducing the risk of healthcare systems collapsing during the outbreak stage.

In this section, we accomplish the following two tasks. Firstly, we introduce the idea of predicting the number of infected cases and show the principle of the relevant models. Secondly, based on the number of infected cases, ICU healthcare resources are divided into two categories (healthcare workers and healthcare supplies), and their respective demand forecasting models are constructed.

Prediction model for the number of infected cases

Gasvr model.

Support vector machine (SVM) is a machine-learning language for classification developed by Vapnik [ 29 ]. Suppose there are two categories of samples: H1 and H2. If hyperplane H is able to correctly classify the samples into these two categories and maximize the margin between the two categories, it is known as the optimal separating hyperplane (OSH). The sample vectors closest to the OSH in H1 and H2 are known as the support vectors. To apply SVM to prediction, it is essential to perform regression fitting. By introducing the $\varepsilon$ -insensitive loss function, SVM can be converted to a support vector regression machine, where the role of the OSH is to minimize the error of all samples from this plane. SVR has a theoretical basis in statistical learning and relatively high learning performance, making it suitable for performing predictions in small-sample, non-linear, and multi-dimensional fields [ 30 , 31 ].

Assume the training sample set containing $l$ training samples is given by $\{({x}_{i},{y}_{i}),i=\mathrm{1,2},...,l\}$ , where ${x}_{i}=[{x}_{i}^{1},{x}_{i}^{2},...,{x}_{i}^{d}{]}^{\rm T}$ and ${y}_{i}\in R$ are the corresponding output values.

Let the regression function be $f(x)=w\Phi (x)+b$ , where $\phi (x)$ is the non-linear mapping function. The linear $\varepsilon$ -insensitive loss function is defined as shown in formula ( 1 ).

Among the rest, $f(x)$ is the predicted value returned by the regression function, and $y$ is the corresponding real value. If the error between $f(x)$ and $y$ is ≤ $\varepsilon$ , the loss is 0; otherwise, the loss is $\left|y-f(x)\right|-\varepsilon$ .

The slack variables ${\xi }_{i}$ and ${\xi }_{i}^{*}$ are introduced, and $w$ , $b$ are solved using the following equation as shown in formula ( 2 ).

Among the rest, $C$ is the penalty factor, with larger values indicating a greater penalty for errors > $\varepsilon$ ; $\varepsilon$ is defined as the error requirement, with smaller values indicating a smaller error of the regression function.

The Lagrange function is introduced to solve the above function and transformed into the dual form to give the formula ( 3 ).

Among the rest, $K({x}_{i},{x}_{j})=\Phi ({x}_{i})\Phi ({x}_{j})$ is the kernel function. The kernel function determines the structure of high-dimensional feature space and the complexity of the final solution. The Gaussian kernel is selected for this study with the function $K({x}_{i},{x}_{j})=\mathit{exp}(-\frac{\Vert {x}_{i}-{x}_{j}\Vert }{2{\sigma }^{2}})$ .

Let the optimal solution be $a=[{a}_{1},{a}_{2},...,{a}_{l}]$ and ${a}^{*}=[{a}_{1}^{*},{a}_{2}^{*},...,{a}_{l}]$ to give the formula ( 4 ) and formula ( 5 ).

Among the rest, ${N}_{nsv}$ is the number of support vectors.

In sum, the regression function is as shown in formula ( 6 ).

when some of the parameters are not 0, the corresponding samples are the support vectors in the problem. This is the principle of SVR. The values of the three unknown parameters (penalty factor C, ε -insensitive loss function, and kernel function coefficient $\sigma )$ , can directly impact the model effect. The penalty factor C affects the degree of function fitting through the selection of outliers in the sample by the function. Thus, excessively large values lead to better fit but poorer generalization, and vice versa. The ε value in the ε-insensitive loss function determines the accuracy of the model by affecting the width of support vector selection. Thus, excessively large values lead to lower accuracy that does not meet the requirements and excessively small values are overly complex and increase the difficulty. The kernel function coefficient $\sigma$ determines the distribution and range of the training sample by controlling the size of inner product scaling in high-dimensional space, which can affect overfitting.

Therefore, we introduce other algorithms for optimization of the three parameters in SVR. Currently the commonly used algorithms are 32and some heuristic algorithms. Although the grid search method is able to find the highest classification accuracy, which is the global optimal solution. However, sometimes it can be time-consuming to find the optimal parameters for larger scales. If a heuristic algorithm is used, we could find the global optimal solution without having to trace over all the parameter points in the grid. And GA is one of the most commonly used heuristic algorithms, compared to other heuristic algorithms, it has the advantages of strong global search, generalizability, and broader blending with other algorithms.

Given these factors, we employ a GA to encode and optimize the relevant parameters of the model. The inputs are the experimental training dataset, the Gaussian kernel function expression, the maximum number of generations taken by the GA, the accuracy range of the optimized parameters, the GA population size, the fitness function, the probability of crossover, and the probability of mutation. The outputs are the optimal penalty factor C, ε-insensitive loss function parameter $\varepsilon ,$ and optimal Gaussian kernel parameter $\sigma$ of SVR, thus achieving the optimization of SVR. The basic steps involved in GA optimization are described in detail below, and the model prediction process is shown in Fig. 1 .

Prediction process of the GASVR model

Population initialization

The three parameters are encoded using binary arrays composed of 0–1 bit-strings. Each parameter consisted of six bits, and the initial population is randomly generated. The population size is set at 60, and the number of iterations is 200.

Fitness calculation

In the same dataset, the K-fold cross-validation technique is used to test each individual in the population, with K = 5. K-fold cross validation effectively avoids the occurrence of model over-learning and under-learning. For the judgment of the individual, this paper evaluates it in terms of fitness calculations. Therefore, combining the two enables the effective optimization of the model’s selected parameters and improves the accuracy of regression prediction.

Fitness is calculated using the mean error method, with smaller mean errors indicating better fitness. The fitness function is shown in formula ( 7 ) [ 32 ].

The individual’s genotype is decoded and mapped to the corresponding parameter value, which is substituted into the SVR model for training. The parameter optimization range is 0.01 ≤ C ≤ 100, 0.1 ≤ $\sigma$ ≤ 20, and 0.001 ≤ ε ≤ 1.

Selection: The selection operator is performed using the roulette wheel method.

Crossover: The multi-point crossover operator, in which two chromosomes are selected and multiple crossover points are randomly chosen for swapping, is employed. The crossover probability is set at 0.9.

Mutation: The inversion mutation operator, in which two points are randomly selected and the gene values between them are reinserted to the original position in reverse order, is employed. The mutation probability is set at 0.09.

Decoding: The bit strings are converted to parameter sets.

The parameter settings of the GASVR model built in this paper are shown in Table 1 .

BILSTM model

The LSTM model is a special recurrent neural network algorithm that can remember the long-term dependencies of data series and has an excellent capacity for self-learning and non-linear fitting. LSTM automatically connects hidden layers across time points, such that the output of one time point can arbitrarily enter the output terminal or the hidden layer of the next time point. Therefore, it is suitable for the sample prediction of time-series data and can predict future data based on stored data. Details of the model are shown in Fig. 2 .

Schematic diagram of the LSTM model

LSTM consists of a forget gate, an input gate, and an output gate.

The forget gate combines the previous and current time steps to give the output of the sigmoid activation function. Its role is to screen the information from the previous state and identify useful information that truly impacts the subsequent time step. The equation for the forget gate is shown in formula ( 8 ).

Among the number, $W_{f}$ is the weight of the forget gate, ${b}_{f}$ is the bias, $\sigma$ is the sigmoid activation function, ${f}_{t}$ is the output of the sigmoid activation function, $t-1$ is the previous time step, $t$ is the current time step, and ${x}_{t}$ is the input time-series data at time step $t$ .

The input gate is composed of the output of the sigmoid and tanh activation functions, and its role is to control the ratio of input information entering the information of a given time step. The equation for the input gate is shown in formula ( 9 ).

Among the number, ${W}_{i}$ is the output weight of the input gate, ${i}_{t}$ is the output of the sigmoid activation function, ${b}_{i}$ and ${b}_{C}$ are the biases of the input gate, and ${W}_{C}$ is the output of the tanh activation function.

The role of the output gate is to control the amount of information output at the current state, and its equation is shown in formula ( 10 ).

Among the number, ${W}_{o}$ is the weight of ${o}_{t}$ , and ${b}_{o}$ is the bias of the output gate.

The values of the above activation functions $\sigma$ and tanh are generally shown in formulas ( 11 ) and ( 12 ).

${C}_{t}$ is the data state of the current time step, and its value is determined by the input information of the current state and the information of the previous state. It is shown in formula ( 13 ).

Among the number, $\widetilde{{C}_{t}}=\mathit{tan}h({W}_{c}[{h}_{t-1},{x}_{t}]+{b}_{c})$ .

${h}_{t}$ is the state information of the hidden layer at the current time step, ${h}_{t}={o}_{t}\times \mathit{tan}h({c}_{t})$ .Each time step ${T}_{n}$ has a corresponding state ${C}_{t}$ . By undergoing the training process, the model can learn how to modify state ${C}_{t}$ through the forget, output, and input gates. Therefore, this state is consistently passed on, implying that important distant information will neither be forgotten nor significantly affected by unimportant information.

The above describes the principle of LSTM, which involves forward processing when applied. BILSTM consists of two LSTM networks, one of which processes the input sequence in the forward direction (i.e., the original order), while the other inputs the time series in the backward direction into the LSTM model. After processing both LSTM networks, the outputs are combined, which eventually gives the output results of the BILSTM model. Details of the model are presented in Fig. 3 .

Schematic diagram of the BILSTM model

Compared to LSTM, BILSTM can achieve bidirectional information extraction of the time-series and connect the two LSTM layers onto the same output layer. Therefore, in theory, its predictive performance should be superior to that of LSTM. In BILSTM, the equations of the forward hidden layer( $\overrightarrow{{h}_{t}}$ ) , backward hidden layer( $\overleftarrow{{h}_{t}}$ ) , and output layer( ${o}_{t}$ ) are shown in formulas ( 14 ) , ( 15 ) and ( 16 ).

The parameter settings of the BILSTM model built in this paper are shown in Table 2 .

Informer model

The Informer model follows the compiler-interpreter architecture in the Transformer model, and based on this, structural optimizations have been made to reduce the computational time complexity of the algorithm and to optimize the output form of the interpreter. The two optimization methods are described in detail next.

With large amounts of input data, neural network models can have difficulty capturing long-term interdependencies in sequences, which can produce gradient explosions or gradient vanishing and affect the model's prediction accuracy. Informer model solves the existential gradient problem by using a ProbSparse Self-attention mechanism to make more efficient than conventional self-attention.

The value of Transformer self-attention is shown in formula ( 17 ).

Among them, $Q\in {R}^{{L}_{Q}\times d}$ is the query matrix, $K\in {R}^{{L}_{K}\times d}$ is the key matrix, and $V\in {R}^{{L}_{V}\times d}$ is the value matrix, which are obtained by multiplying the input matrix X with the corresponding weight matrices ${W}^{Q}$ , ${W}^{K}$ , ${W}^{V}$ respectively, and d is the dimensionality of Q, K, and V. Let ${q}_{i}$ , ${k}_{i}$ , $v_{i}$ represent the ith row in the Q, K, V matrices respectively, then the ith attention coefficient is shown in formula ( 18 ) as follows.

Therein, $p({k}_{j}|{q}_{i})$ denotes the traditional Transformer's probability distribution formula, and $k({q}_{i},{K}_{l})$ denotes the asymmetric exponential sum function. Firstly, q=1 is assumed, which implies that the value of each moment is equally important; secondly, the difference between the observed distribution and the assumed one is evaluated by the KL scatter, if the value of KL is bigger, the bigger the difference with the assumed distribution, which represents the more important this moment is. Then through inequality $ln{L}_{k}\le M({q}_{i},K)\le {\mathit{max}}_{j}\left\{\frac{{q}_{i}{k}_{j}^{\rm T}}{\sqrt{d}}\right\}-\frac{1}{{L}_{k}}{\sum }_{j=1}^{{L}_{k}}\left\{\frac{{q}_{i}{k}_{j}^{\rm T}}{\sqrt{d}}\right\}+ln{L}_{k}$ , $M({q}_{i},K)$ is transformed into $\overline{M}({q}_{i},K)$ . According to the above steps, the ith sparsity evaluation formula is obtained as shown in formula ( 19 ) [ 33 ].

One of them, $M({q}_{i},K)$ denotes the ith sparsity measure; $\overline{M}({q}_{i},K)$ denotes the ith approximate sparsity measure; ${L}_{k}$ is the length of query vector. $TOP-u$ quantities of $\overline{M}$ are selected to form $\overline{Q}$ , $\overline{Q}$ is the first u sparse matrices, and the final sparse self-attention is shown in Formula ( 20 ). At this point, the time complexity is still $O({n}^{2})$ , and to solve this problem, only l moments of M2 are computed to reduce the time complexity to $O(L\cdot \mathit{ln}(L))$ .

Informer uses a generative decoder to obtain long sequence outputs.Informer uses the standard decoder architecture shown in Fig. 4 , in long time prediction, the input given to the decoder is shown in formula ( 21 ).

Informer uses a generative decoder to obtain long sequence outputs

Therein, ${X}_{de}^{t}$ denotes the input to the decoder; ${X}_{token}^{t}\in {R}^{({L}_{token}+{L}_{y})\times {d}_{\mathit{mod}el}}$ is the dimension of the encoder output, which is the starting token without using all the output dimensions; ${X}_{0}^{t}\in {R}^{({L}_{token}+{L}_{y})\times {d}_{\mathit{mod}el}}$ is the dimension of the target sequence, which is uniformly set to 0; and finally the splicing input is performed to the encoder for prediction.

The parameter settings of Informer model created in this paper are shown in Table 3 .

BILSTM-GASVR combined prediction model

SVR has demonstrated good performance in solving problems like finite samples and non-linearity. Compared to deep learning methods, it offers faster predictions and smaller empirical risks. BILSTM has the capacity for long-term memory, can effectively identify data periodicity and trends, and is suitable for the processing of time-series data. Hence, it can be used to identify the effect of time-series on the number of confirmed cases. Given the advantages of these two methods in different scenarios, we combined them to perform predictions using GASVR, followed by error repair using BILSTM. The basic steps for prediction based on the BILSTM-GASVR model are as follows:

Normalization is performed on the initial data.

The GASVR model is applied to perform training and parameter optimization of the data to obtain the predicted value $\widehat{{y}_{i}}$ .

After outputting the predicted value of GASVR, the residual sequence between the predicted value and real data is extracted to obtain the error ${\gamma }_{i}$ (i.e., ${\gamma }_{i}={y}_{i}-\widehat{{y}_{i}}$ ).

The BILSTM model is applied to perform training of the error to improve prediction accuracy. The BILSTM model in this paper is a multiple input single output model. Its inputs are the true and predicted error values ${\gamma }_{i}$ and its output is the new error value $\widehat{{\gamma }_{i}}$ predicted by BILSTM.

The final predicted value is the sum of the GASVR predicted value and the BILSTM residual predicted value (i.e., ${Y}_{i}=\widehat{{y}_{i}}+\widehat{{\gamma }_{i}}$ ).

The parameter settings of the BILSTM-GASVR model built in this paper are shown in Table 4 .

Model testing criteria

To test the effect of the model, the prediction results of the BILSTM-GASVR model are compared to those of GASVR, LSTM, BILSTM and Informer. The prediction error is mainly quantified using three indicators: mean squared error (MSE), root mean squared error (RMSE), and correlation coefficient ( $R^{2}$ ). Their respective equations are shown in formulas ( 22 ), ( 23 ) and ( 24 ).

Demand forecasting model of ICU healthcare resources

ICU healthcare resources can be divided into human and material resources. Human resources refer specifically to the professional healthcare workers in the ICU. Material resources, which are combined with the actual consumption of medical supplies, can be divided into consumables and non-consumables. Consumables refer to the commonly used drugs in the ICU, which include drugs for treating cardiac insufficiency, vasodilators, anti-shock vasoactive drugs, analgesics, sedatives, muscle relaxants, anti-asthmatic drugs, and anticholinergics. Given that public health emergencies have a relatively high probability of affecting the respiratory system, we compiled a list of commonly used drugs for respiratory diseases in the ICU (Table 5 ).

Non-consumables refer to therapeutic medical equipment, including electrocardiogram machines, blood gas analyzers, electrolyte analyzers, bedside diagnostic ultrasound machines, central infusion workstations, non-invasive ventilators, invasive ventilators, airway clearance devices, defibrillators, monitoring devices, cardiopulmonary resuscitation devices, and bedside hemofiltration devices.

The demand forecasting model of ICU healthcare resources constructed in this study, as well as its relevant parameters and definitions, are described below. ${R}_{ij}^{n}$ is the forecasted demand for the $i$ th category of resources on the $n$ th day in region $j$ . ${Y}_{j}^{n}$ is the predicted number of current confirmed cases on the $n$ th day in region $j$ . ${M}_{j}^{n}$ is the number of ICU healthcare workers on the $n$ th day in region $j$ , which is given by the following formula: number of healthcare workers the previous day + number of new recruits − reduction in number the previous day, where the reduction in number refers to the number of healthcare workers who are unable to work due to infection or overwork. In general, the number of ICU healthcare workers should not exceed 5% of the number of current confirmed cases (i.e., it takes the value range [0, $Y_{j}^{n}$ ×5%]). $U_{i}$ is the maximum working hours or duration of action of the $i$ th resource category within one day. ${A}_{j}$ is the number of resources in the $i$ th category allocated to patients (i.e., how many units of resources in the $i$ th category is needed for a patient who need the $i$ th unit of the given resource). ${\varphi }_{i}$ is the demand conversion coefficient (i.e., the proportion of the current number of confirmed cases who need to use the $i$ th resource category). ${C}_{ij}^{n}$ is the available quantity of material resources of the $i$ th category on the $n$ th day in region $j$ . At the start, this quantity is the initial reserve, and once the initial reserve is exhausted, it is the surplus from the previous day. The formula for this parameter is given as follows: available quantity from the previous day + replenishment on the previous day − quantity consumed on the previous day, where if ${C}_{ij}^{n}$ is a negative number, it indicates the amount of shortage for the given category of resources on the previous day.

In summary, the demand forecast for emergency medical supplies constructed in this study is shown in formula ( 25 ).

The number of confirmed cases based on data-driven prediction is introduced into the demand forecasting model for ICU resources to forecast the demand for the various categories of resources. In addition to the number of current confirmed cases, the main variables of the first demand forecasting model for human resources are the available quantity and maximum working hours. The main variable of the second demand forecasting model for consumable resources is the number of units consumed by the available quantity. The main variable of the third model for non-consumable resources is the allocated quantity. These three resource types can be predicted using the demand forecasting model constructed in this study.

Prediction of the number of current infected cases

The COVID-19 situation in Shanghai is selected for our experiment. A total of 978 entries of epidemic-related data in Shanghai between January 20, 2020, and September 24, 2022, are collected from the epidemic reporting platform. This dataset is distributed over a large range and belongs to a right-skewed leptokurtic distribution. The specific statistical description of data is shown in Table 6 . Part of the data is shown in Table 7 .

And we divided the data training set and test set in an approximate 8:2 ratio, namely, 798 days for training (January 20, 2020 to March 27, 2022) and 180 days for prediction (March 28, 2022 to September 24, 2022).

Due to the large difference in order of magnitude between the various input features, directly implementing training and model construction would lead to suboptimal model performance. Such effects are usually eliminated through normalization. In terms of interval selection, [0, 1] reflects the probability distribution of the sample, whereas [-1, 1] mostly reflects the state distribution or coordinate distribution of the sample. Therefore, [-1, 1] is selected for the normalization interval in this study, and the processing method is shown in formula ( 26 ).

Among the rest, $X$ is the input sample, ${X}_{min}$ and ${X}_{max}$ are the minimum and maximum values of the input sample, and ${X}_{new}$ is the input feature after normalization.

In addition, we divide the data normalization into two parts, considering that the amount of data in the training set is much more than the test set in the real operating environment. In the first step, we normalize the training set data directly according to the above formula; in the second step, we normalize the test data set using the maximum and minimum values of the training data set.

The values of the preprocessed data are inserted into the GASVR, LSTM, Informer, BILSTM models and the BILSTM-GASVR model is constructed. Figures 5 , 6 , 7 , 8 and 9 show the prediction results. From Figs. 5 , 6 , and 7 , it can be seen that in terms of data accuracy, GASVR more closely matches the real number of infected people relative to BILSTM and LSTM. Especially in the most serious period of the epidemic in Shanghai (April 17, 2022 to April 30, 2022), the advantage of the accuracy of the predicted data of GASVR is even more obvious, which is due to the characteristics of GASVR for small samples and nonlinear prediction. However, in the overall trend of the epidemic, BILSTM and LSTM, which have the ability to learn and memorize to process time series data, are superior. It is clearly seen that in April 1, 2022-April 7, 2022 and May 10, 2022-May 15, 2022, there is a sudden and substantial increase in GASVR in these two time phases, and a sudden and substantial decrease in April 10, 2022-April 14, 2022. These errors also emphasize the stability of BILSTM and LSTM, which are more closely matched to the real epidemic development situation in the whole process of prediction, and the difference between BILSTM and LSTM prediction is that the former predicts data more accurately than the latter, which is focused on the early stage of prediction as well as the peak period of the epidemic. Informer is currently an advanced time series forecasting method. From Fig. 8 , it can be seen that the prediction data accuracy and the overall trend of the epidemic are better than the single prediction models of GASVR, LSTM and BILSTM. However, Informer is more suitable for long time series and more complex and large prediction problems, so the total sample size of less than one thousand cases is not in the comfort zone of Informer model. Figure 9 shows that the BILSTM-GASVR model constructed in this paper is more suitable for this smaller scale prediction problem, with the best prediction results, closest to the actual parameter (number of current confirmed cases), demonstrating small sample and time series advantages. In Short, the prediction effect of models is ranked as follows: BILSTM-GASVR> Informer> GASVR> BILSTM> LSTM.

The prediction result of the GASVR model

The prediction result of the LSTM model

The prediction result of the BILSTM model

The prediction result of the Informer model

The prediction result of the BILSTM-GASVR model

The values of the three indicators (MSE, RMSE, and correlation coefficient ${R}^{2}$ ) for the five models are shown in Table 8 . MSE squares the error so that the larger the model error, the larger the value, which help capture the model's prediction error more sensitively. RMSE is MSE with a root sign added to it, which allows for a more intuitive representation of the order of magnitude difference from the true value. ${R}^{2}$ is a statistical indicator used to assess the overall goodness of fit of the model, which reflects the overall consistency of the predicted trend and does not specifically reflect the degree of data. The results in the Table 8 are consistent with the prediction results in the figure above, while the ranking of MSE, RMSE, and ${R}^{2}$ are also the same (i.e., BILSTM-GASVR> Informer> GASVR> BILSTM> LSTM).

In addition, we analyze the five model prediction data using significance tests as a way of demonstrating whether the model used is truly superior to the other baseline models. The test dataset with kurtosis higher than 4 does not belong to the approximate normal distribution, so parametric tests are not used in this paper. Given that the datasets predicted by each of the five models are continuous and independent datasets, this paper uses the Kruskal-Wallis test, which is a nonparametric test. The test steps are as follows.

Determine hypotheses (H0, H1) and significance level ( $\alpha$ ).

For each data set, all its sample data are combined and ranked from smallest to largest. Then find the number of data items ( ${n}_{i}$ ), rank sum ( ${R}_{i}$ ) and mean rank of each group of data respectively.

Based on the rank sum, the test statistic (H) is calculated for each data set in the Kruskal-Wallis test. The specific calculation is shown in formula ( 27 ).

According to the test statistic and degrees of freedom, find the corresponding p-value in the Kruskal-Wallis distribution table. Based on the P-value, determine whether the original hypothesis is valid.

In the significance test, we set the significance setting original hypothesis (H0) as there is no significant difference between the five data sets obtained from the five predictive models. We set the alternative hypothesis (H1) as there is a significant difference between the five data sets obtained from the five predictive models. At the same time, we choose the most commonly used significance level taken in the significance test, namely 0.05. In this paper, multiple comparisons and two-by-two comparisons of the five data sets obtained from the five predictive models are performed through the SPSS software. The results of the test show that in the multiple comparison session, P=0.001<0.05, so H0 is rejected, which means that the difference between the five groups of data is significant. In the two-by-two comparison session, BILSTM-GASVR is less than 0.05 from the other four prediction models. The specific order of differences is Informer < GASVR < BILSTM < LSTM, which means that the BILSTM-GASVR prediction model does get a statistically significant difference between the dataset and the other models.

In summary, combined prediction using the BILSTM-GASVR model is superior to the other four single models in various aspects in the case study analysis of Shanghai epidemic with a sample size of 978.

Demand forecasting of ICU healthcare resources

Combined with the predicted number of current infected cases, representatives are selected from the three categories of resources for forecasting. The demand for nurses is selected as the representative for the first category of resources.

In view of the fact that there are currently no specific medications that are especially effective for this public health emergency, many ICU treatment measures involved helping patients survive as their own immune systems eliminated the virus. This involved, for example, administering antibiotics when patients developed a secondary bacterial infection. glucocorticoids are used to temporarily suppress the immune system when their immune system attacked and damaged lung tissues causing patients to have difficulty breathing. extracorporeal membrane oxygenation (ECMO) is used for performing cardiopulmonary resuscitation when patients are suffering from cardiac arrest. In this study, we take dexamethasone injection (5 mg), a typical glucocorticoid drug, as the second category of ICU resources (i.e., drugs); and invasive ventilators as the third category of ICU resources (i.e., medical equipment).

During the actual epidemic in Shanghai, the municipal government organized nine critical care teams, which are stationed in eight municipally designated hospitals and are dedicated to the treatment of critically ill patients. In this study, the ICU nurses, dexamethasone injections, and invasive ventilators in Shanghai are selected as the prediction targets and introduced into their respective demand forecasting models. Forecasting of ICU healthcare resources is then performed for the period from March 28, 2022, to April 28, 2022, as an example. Part of the parameter settings for the three types of resources are shown in Tables 9 , 10 , and 11 , respectively.

Table 12 shows the forecasting results of the demand for ICU nurses, dexamethasone injections, and invasive ventilators during the epidemic wave in Shanghai between March 28, 2022, and April 28, 2022.

For the first category (i.e., ICU nurses), human resource support is only needed near the peak period, but the supply could not be replenished immediately. In the early stages, Shanghai could only rely on the nurses’ perseverance, alleviating the shortage of human resources by reducing the number of shifts and increasing working hours. This situation persisted until about April 10 and is only resolved when nurses from other provinces and regions successively arrived in Shanghai.

The second category of ICU resources is drugs, which are rapidly consumed. The pre-event reserve of 30,000 dexamethasone injections could only be maintained for a short period and is fully consumed during the outbreak. Furthermore, daily replenishment is still needed, even when the epidemic has passed its peak and begun its decline.

The third category is invasive ventilators, which are non-consumables. Thus, the reserve lasted for a relatively long period of time in the early stages and did not require replenishment after its maximum usage during the peak period.

Demand forecasting models are constructed based on the classification of healthcare resources according to their respective features. We choose ICU nurses, dexamethasone injections, and invasive ventilators as examples, and then forecast demand for the epidemic wave in Shanghai between March 28, 2022, and April 28, 2022. The main conclusions are as follows:

A long period of time is needed to train ICU healthcare workers who can independently be on duty, taking at least one year from graduation to entering the hospital, in addition to their requiring continuous learning, regular theoretical training, and the accumulation of clinical experience during this process. Therefore, for the first category of ICU healthcare resources, in the long term, healthcare institutions should place a greater emphasis on their talent reserves. Using China as an example, according to the third ICU census, the ratio of the number of ICU physicians to the number of beds is 0.62:1 and the ratio of the number of nurses to the number of beds is 1.96:1, which are far lower than those stipulated by China itself and those of developed countries. Therefore, a fundamental solution is to undertake proactive and systematic planning and construction to ensure the more effective deployment of human resources in the event of a severe outbreak. In the short term, healthcare institutions should focus on the emergency expansion capacity of their human resources. In case there are healthcare worker shortages during emergencies, the situation can be alleviated by summoning retired workers back to work and asking senior medical students from various universities to help in the hospitals to prevent the passive scenario of severely compressing the rest time of existing staff or waiting for external aid. However, it is worth noting that to ensure the effectiveness of such a strategy of using retired healthcare workers or senior students of university medical faculties, it is necessary for healthcare organizations to provide them with regular training in the norm, such as organizing 2-3 drills a year, to ensure the professionalism and proficiency of healthcare workers who are temporarily and suddenly put on the job. At the same time, it is also necessary to fully mobilize the will of individuals. Medical institutions can provide certain subsidies to retired health-care workers and award them with honorable titles. For senior university medical students, volunteer certificates are issued and priority is given to their internships, so that health-care workers can be motivated to self-realization through spiritual and material rewards.

Regarding the second category of ICU resources (i.e., drugs), healthcare institutions perform the subdivision of drug types and carry out dynamic physical preparations based on 15–20% of the service recipient population for clinically essential drugs. This will enable a combination of good preparedness during normal times and emergency situations. In addition, in-depth collaboration with corporations is needed to fully capitalize on their production capacity reserves. This helps medical institutions to be able to scientifically and rationally optimize the structure and quantity of their drug stockpiles to prevent themselves from being over-stressed. Yet the lower demand for medicines at the end of the epidemic led to the problem of excess inventory of enterprises at a certain point in time must be taken into account. So, the medical institutions should sign a strategic agreement on stockpiling with enterprises, take the initiative to bear the guaranteed acquisition measures, and consider the production costs of the cooperative enterprises. These measures are used to truly safeguard the enthusiasm of the cooperative enterprises to invest in the production capacity.

Regarding the third category of ICU resources (i.e., medical equipment), large-scale medical equipment cannot be rapidly mass-produced due to limitations in the capacity for emergency production and conversion of materials. In addition, the bulk procurement of high-end medical equipment is also relatively difficult in the short term. Therefore, it is more feasible for healthcare institutions to have physical reserves of medical equipment, such as invasive ventilators. However, the investment costs of medical equipment are relatively high. Ventilators, for example, cost up to USD $50,000, and subsequent maintenance costs are also relatively high. After all, according to the depreciable life of specialized hospital equipment, the ventilator, as a surgical emergency equipment, is depreciated over five years. And its depreciation rate is calculated at 20% annually for the first five years, which means a monthly depreciation of $835. Thus, the excessively low utilization rate of such equipment will also impact the hospital. Healthcare institutions should, therefore, conduct further investigations on the number of beds and the reserves of ancillary large-scale medical equipment to find a balance between capital investment and patient needs.

The limitations of this paper are reflected in the following three points. Firstly, in the prediction of the number of infections, the specific research object in this paper is COVID-19, and other public health events such as SARS, H1N1, and Ebola are not comparatively analyzed. The main reason for this is the issue of data accessibility, and it is easier for us to analyze events that have occurred in recent years. In addition, using the Shanghai epidemic as a specific case may be more representative of the epidemic situation in an international metropolis with high population density and mobility. Hence, it has certain regional limitations, and subsequent studies should expand the scope of the case study to reflect the characteristics of epidemic transmission in different types of urban areas and enhance the generalizability.

Secondly, the main emphasis of this study is on forecasting the demand for ICU healthcare resources across the entire region of the epidemic, with a greater focus on patient demand during public emergencies. Our aims are to help all local healthcare institutions more accurately identify changes in ICU healthcare resource demand during this local epidemic wave, gain a more accurate understanding of the treatment demands of critically ill patients, and carry out comprehensive, scientifically based decision-making. Therefore, future studies can examine individual healthcare institutions instead and incorporate the actual conditions of individual units to construct multi-objective models. In this way, medical institutions can further grasp the relationship between different resource inputs and the recovery rate of critically ill patients, and achieve the balance between economic and social benefits.

Finally, for the BILSTM-GASVR prediction method, in addition to the number of confirmed diagnoses predicted for an outbreak in a given region, other potential applications beyond this type of medium-sized dataset still require further experimentation. For example, whether the method is suitable for procurement planning of a certain supply in production management, forecasting of goods sales volume in marketing management, and other long-period, large-scale and other situations.

Within the context of major public health events, the fluctuations and uncertainties in the demand for ICU resources can lead to large errors between the healthcare supply and actual demand. Therefore, this study focuses on the question of forecasting the demand for ICU healthcare resources. Based on the number of current confirmed cases, we construct the BILSTM-GASVR model for predicting the number of patients. By comparing the three indicators (MSE, MAPE, and correlation coefficient $R^{2}$ ) and the results of the BILSTM, LSTM, and GASVR models, we demonstrate that our model have a higher accuracy. Our findings can improve the timeliness and accuracy of predicting ICU healthcare resources and enhance the dynamics of demand forecasting. Hence, this study may serve as a reference for the scientific deployment of ICU resources in healthcare institutions during major public events.

Given the difficulty in data acquisition, only the Shanghai epidemic dataset is selected in this paper, which is one of the limitations mentioned in Part 4. Although the current experimental cases of papers in the same field do not fully conform to this paper, the results of the study cannot be directly compared. However, after studying the relevant reviews and the results of the latest papers, we realize that there is consistency in the prediction ideas and prediction methods [ 34 , 35 ]. Therefore, we summarize the similarities and differences between the results of the study and other research papers in epidemic forecasting as shown below.

Similarities: on the one hand, we all characterize trends in the spread of the epidemic and predict the number of infections over 14 days. On the other hand, we all select the current mainstream predictive models as the basis and combine or improve them. Moreover, we all use the same evaluation method (comparison of metrics such as MSE and realistic values) to evaluate the improvements against other popular predictive models.

Differences: on the one hand, other papers focus more on predictions at the point of the number of patients, such as hospitalization rate, number of infections, etc. This paper extends the prediction from the number of patients to the specific healthcare resources. This paper extends the prediction from the number of patients to specific healthcare resources. We have divided the medical resources and summarized the demand regularities of the three types of information in the epidemic, which provides the basis for decision-making on epidemic prevention to the government or medical institutions. On the other hand, in addition to the two assessment methods mentioned in the same point, this paper assesses the performance of the prediction methods with the help of significance tests, which is a statistical approach to data. This can make the practicality of the forecasting methodology more convincing.

Availability of data and materials

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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Zhang, W., Li, X. A data-driven combined prediction method for the demand for intensive care unit healthcare resources in public health emergencies. BMC Health Serv Res 24 , 477 (2024). https://doi.org/10.1186/s12913-024-10955-8

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What the data says about abortion in the U.S.

Pew Research Center has conducted many surveys about abortion over the years, providing a lens into Americans’ views on whether the procedure should be legal, among a host of other questions.

In a Center survey conducted nearly a year after the Supreme Court’s June 2022 decision that ended the constitutional right to abortion , 62% of U.S. adults said the practice should be legal in all or most cases, while 36% said it should be illegal in all or most cases. Another survey conducted a few months before the decision showed that relatively few Americans take an absolutist view on the issue .

Find answers to common questions about abortion in America, based on data from the Centers for Disease Control and Prevention (CDC) and the Guttmacher Institute, which have tracked these patterns for several decades:

How many abortions are there in the U.S. each year?

How has the number of abortions in the u.s. changed over time, what is the abortion rate among women in the u.s. how has it changed over time, what are the most common types of abortion, how many abortion providers are there in the u.s., and how has that number changed, what percentage of abortions are for women who live in a different state from the abortion provider, what are the demographics of women who have had abortions, when during pregnancy do most abortions occur, how often are there medical complications from abortion.

This compilation of data on abortion in the United States draws mainly from two sources: the Centers for Disease Control and Prevention (CDC) and the Guttmacher Institute, both of which have regularly compiled national abortion data for approximately half a century, and which collect their data in different ways.

The CDC data that is highlighted in this post comes from the agency’s “abortion surveillance” reports, which have been published annually since 1974 (and which have included data from 1969). Its figures from 1973 through 1996 include data from all 50 states, the District of Columbia and New York City – 52 “reporting areas” in all. Since 1997, the CDC’s totals have lacked data from some states (most notably California) for the years that those states did not report data to the agency. The four reporting areas that did not submit data to the CDC in 2021 – California, Maryland, New Hampshire and New Jersey – accounted for approximately 25% of all legal induced abortions in the U.S. in 2020, according to Guttmacher’s data. Most states, though, do have data in the reports, and the figures for the vast majority of them came from each state’s central health agency, while for some states, the figures came from hospitals and other medical facilities.

Discussion of CDC abortion data involving women’s state of residence, marital status, race, ethnicity, age, abortion history and the number of previous live births excludes the low share of abortions where that information was not supplied. Read the methodology for the CDC’s latest abortion surveillance report , which includes data from 2021, for more details. Previous reports can be found at stacks.cdc.gov by entering “abortion surveillance” into the search box.

For the numbers of deaths caused by induced abortions in 1963 and 1965, this analysis looks at reports by the then-U.S. Department of Health, Education and Welfare, a precursor to the Department of Health and Human Services. In computing those figures, we excluded abortions listed in the report under the categories “spontaneous or unspecified” or as “other.” (“Spontaneous abortion” is another way of referring to miscarriages.)

Guttmacher data in this post comes from national surveys of abortion providers that Guttmacher has conducted 19 times since 1973. Guttmacher compiles its figures after contacting every known provider of abortions – clinics, hospitals and physicians’ offices – in the country. It uses questionnaires and health department data, and it provides estimates for abortion providers that don’t respond to its inquiries. (In 2020, the last year for which it has released data on the number of abortions in the U.S., it used estimates for 12% of abortions.) For most of the 2000s, Guttmacher has conducted these national surveys every three years, each time getting abortion data for the prior two years. For each interim year, Guttmacher has calculated estimates based on trends from its own figures and from other data.

The latest full summary of Guttmacher data came in the institute’s report titled “Abortion Incidence and Service Availability in the United States, 2020.” It includes figures for 2020 and 2019 and estimates for 2018. The report includes a methods section.

In addition, this post uses data from StatPearls, an online health care resource, on complications from abortion.

An exact answer is hard to come by. The CDC and the Guttmacher Institute have each tried to measure this for around half a century, but they use different methods and publish different figures.

The last year for which the CDC reported a yearly national total for abortions is 2021. It found there were 625,978 abortions in the District of Columbia and the 46 states with available data that year, up from 597,355 in those states and D.C. in 2020. The corresponding figure for 2019 was 607,720.

The last year for which Guttmacher reported a yearly national total was 2020. It said there were 930,160 abortions that year in all 50 states and the District of Columbia, compared with 916,460 in 2019.

How the CDC gets its data: It compiles figures that are voluntarily reported by states’ central health agencies, including separate figures for New York City and the District of Columbia. Its latest totals do not include figures from California, Maryland, New Hampshire or New Jersey, which did not report data to the CDC. ( Read the methodology from the latest CDC report .)
How Guttmacher gets its data: It compiles its figures after contacting every known abortion provider – clinics, hospitals and physicians’ offices – in the country. It uses questionnaires and health department data, then provides estimates for abortion providers that don’t respond. Guttmacher’s figures are higher than the CDC’s in part because they include data (and in some instances, estimates) from all 50 states. ( Read the institute’s latest full report and methodology .)

While the Guttmacher Institute supports abortion rights, its empirical data on abortions in the U.S. has been widely cited by groups and publications across the political spectrum, including by a number of those that disagree with its positions .

These estimates from Guttmacher and the CDC are results of multiyear efforts to collect data on abortion across the U.S. Last year, Guttmacher also began publishing less precise estimates every few months , based on a much smaller sample of providers.

The figures reported by these organizations include only legal induced abortions conducted by clinics, hospitals or physicians’ offices, or those that make use of abortion pills dispensed from certified facilities such as clinics or physicians’ offices. They do not account for the use of abortion pills that were obtained outside of clinical settings .

(Back to top)

A line chart showing the changing number of legal abortions in the U.S. since the 1970s.

The annual number of U.S. abortions rose for years after Roe v. Wade legalized the procedure in 1973, reaching its highest levels around the late 1980s and early 1990s, according to both the CDC and Guttmacher. Since then, abortions have generally decreased at what a CDC analysis called “a slow yet steady pace.”

Guttmacher says the number of abortions occurring in the U.S. in 2020 was 40% lower than it was in 1991. According to the CDC, the number was 36% lower in 2021 than in 1991, looking just at the District of Columbia and the 46 states that reported both of those years.

(The corresponding line graph shows the long-term trend in the number of legal abortions reported by both organizations. To allow for consistent comparisons over time, the CDC figures in the chart have been adjusted to ensure that the same states are counted from one year to the next. Using that approach, the CDC figure for 2021 is 622,108 legal abortions.)

There have been occasional breaks in this long-term pattern of decline – during the middle of the first decade of the 2000s, and then again in the late 2010s. The CDC reported modest 1% and 2% increases in abortions in 2018 and 2019, and then, after a 2% decrease in 2020, a 5% increase in 2021. Guttmacher reported an 8% increase over the three-year period from 2017 to 2020.

As noted above, these figures do not include abortions that use pills obtained outside of clinical settings.

Guttmacher says that in 2020 there were 14.4 abortions in the U.S. per 1,000 women ages 15 to 44. Its data shows that the rate of abortions among women has generally been declining in the U.S. since 1981, when it reported there were 29.3 abortions per 1,000 women in that age range.

The CDC says that in 2021, there were 11.6 abortions in the U.S. per 1,000 women ages 15 to 44. (That figure excludes data from California, the District of Columbia, Maryland, New Hampshire and New Jersey.) Like Guttmacher’s data, the CDC’s figures also suggest a general decline in the abortion rate over time. In 1980, when the CDC reported on all 50 states and D.C., it said there were 25 abortions per 1,000 women ages 15 to 44.

That said, both Guttmacher and the CDC say there were slight increases in the rate of abortions during the late 2010s and early 2020s. Guttmacher says the abortion rate per 1,000 women ages 15 to 44 rose from 13.5 in 2017 to 14.4 in 2020. The CDC says it rose from 11.2 per 1,000 in 2017 to 11.4 in 2019, before falling back to 11.1 in 2020 and then rising again to 11.6 in 2021. (The CDC’s figures for those years exclude data from California, D.C., Maryland, New Hampshire and New Jersey.)

The CDC broadly divides abortions into two categories: surgical abortions and medication abortions, which involve pills. Since the Food and Drug Administration first approved abortion pills in 2000, their use has increased over time as a share of abortions nationally, according to both the CDC and Guttmacher.

The majority of abortions in the U.S. now involve pills, according to both the CDC and Guttmacher. The CDC says 56% of U.S. abortions in 2021 involved pills, up from 53% in 2020 and 44% in 2019. Its figures for 2021 include the District of Columbia and 44 states that provided this data; its figures for 2020 include D.C. and 44 states (though not all of the same states as in 2021), and its figures for 2019 include D.C. and 45 states.

Guttmacher, which measures this every three years, says 53% of U.S. abortions involved pills in 2020, up from 39% in 2017.

Two pills commonly used together for medication abortions are mifepristone, which, taken first, blocks hormones that support a pregnancy, and misoprostol, which then causes the uterus to empty. According to the FDA, medication abortions are safe until 10 weeks into pregnancy.

Surgical abortions conducted during the first trimester of pregnancy typically use a suction process, while the relatively few surgical abortions that occur during the second trimester of a pregnancy typically use a process called dilation and evacuation, according to the UCLA School of Medicine.

In 2020, there were 1,603 facilities in the U.S. that provided abortions, according to Guttmacher . This included 807 clinics, 530 hospitals and 266 physicians’ offices.

A horizontal stacked bar chart showing the total number of abortion providers down since 1982.

While clinics make up half of the facilities that provide abortions, they are the sites where the vast majority (96%) of abortions are administered, either through procedures or the distribution of pills, according to Guttmacher’s 2020 data. (This includes 54% of abortions that are administered at specialized abortion clinics and 43% at nonspecialized clinics.) Hospitals made up 33% of the facilities that provided abortions in 2020 but accounted for only 3% of abortions that year, while just 1% of abortions were conducted by physicians’ offices.

Looking just at clinics – that is, the total number of specialized abortion clinics and nonspecialized clinics in the U.S. – Guttmacher found the total virtually unchanged between 2017 (808 clinics) and 2020 (807 clinics). However, there were regional differences. In the Midwest, the number of clinics that provide abortions increased by 11% during those years, and in the West by 6%. The number of clinics decreased during those years by 9% in the Northeast and 3% in the South.

The total number of abortion providers has declined dramatically since the 1980s. In 1982, according to Guttmacher, there were 2,908 facilities providing abortions in the U.S., including 789 clinics, 1,405 hospitals and 714 physicians’ offices.

The CDC does not track the number of abortion providers.

In the District of Columbia and the 46 states that provided abortion and residency information to the CDC in 2021, 10.9% of all abortions were performed on women known to live outside the state where the abortion occurred – slightly higher than the percentage in 2020 (9.7%). That year, D.C. and 46 states (though not the same ones as in 2021) reported abortion and residency data. (The total number of abortions used in these calculations included figures for women with both known and unknown residential status.)

The share of reported abortions performed on women outside their state of residence was much higher before the 1973 Roe decision that stopped states from banning abortion. In 1972, 41% of all abortions in D.C. and the 20 states that provided this information to the CDC that year were performed on women outside their state of residence. In 1973, the corresponding figure was 21% in the District of Columbia and the 41 states that provided this information, and in 1974 it was 11% in D.C. and the 43 states that provided data.

In the District of Columbia and the 46 states that reported age data to the CDC in 2021, the majority of women who had abortions (57%) were in their 20s, while about three-in-ten (31%) were in their 30s. Teens ages 13 to 19 accounted for 8% of those who had abortions, while women ages 40 to 44 accounted for about 4%.

The vast majority of women who had abortions in 2021 were unmarried (87%), while married women accounted for 13%, according to the CDC , which had data on this from 37 states.

A pie chart showing that, in 2021, majority of abortions were for women who had never had one before.

In the District of Columbia, New York City (but not the rest of New York) and the 31 states that reported racial and ethnic data on abortion to the CDC , 42% of all women who had abortions in 2021 were non-Hispanic Black, while 30% were non-Hispanic White, 22% were Hispanic and 6% were of other races.

Looking at abortion rates among those ages 15 to 44, there were 28.6 abortions per 1,000 non-Hispanic Black women in 2021; 12.3 abortions per 1,000 Hispanic women; 6.4 abortions per 1,000 non-Hispanic White women; and 9.2 abortions per 1,000 women of other races, the CDC reported from those same 31 states, D.C. and New York City.

For 57% of U.S. women who had induced abortions in 2021, it was the first time they had ever had one, according to the CDC. For nearly a quarter (24%), it was their second abortion. For 11% of women who had an abortion that year, it was their third, and for 8% it was their fourth or more. These CDC figures include data from 41 states and New York City, but not the rest of New York.

A bar chart showing that most U.S. abortions in 2021 were for women who had previously given birth.

Nearly four-in-ten women who had abortions in 2021 (39%) had no previous live births at the time they had an abortion, according to the CDC . Almost a quarter (24%) of women who had abortions in 2021 had one previous live birth, 20% had two previous live births, 10% had three, and 7% had four or more previous live births. These CDC figures include data from 41 states and New York City, but not the rest of New York.

The vast majority of abortions occur during the first trimester of a pregnancy. In 2021, 93% of abortions occurred during the first trimester – that is, at or before 13 weeks of gestation, according to the CDC . An additional 6% occurred between 14 and 20 weeks of pregnancy, and about 1% were performed at 21 weeks or more of gestation. These CDC figures include data from 40 states and New York City, but not the rest of New York.

About 2% of all abortions in the U.S. involve some type of complication for the woman , according to an article in StatPearls, an online health care resource. “Most complications are considered minor such as pain, bleeding, infection and post-anesthesia complications,” according to the article.

The CDC calculates case-fatality rates for women from induced abortions – that is, how many women die from abortion-related complications, for every 100,000 legal abortions that occur in the U.S . The rate was lowest during the most recent period examined by the agency (2013 to 2020), when there were 0.45 deaths to women per 100,000 legal induced abortions. The case-fatality rate reported by the CDC was highest during the first period examined by the agency (1973 to 1977), when it was 2.09 deaths to women per 100,000 legal induced abortions. During the five-year periods in between, the figure ranged from 0.52 (from 1993 to 1997) to 0.78 (from 1978 to 1982).

The CDC calculates death rates by five-year and seven-year periods because of year-to-year fluctuation in the numbers and due to the relatively low number of women who die from legal induced abortions.

In 2020, the last year for which the CDC has information , six women in the U.S. died due to complications from induced abortions. Four women died in this way in 2019, two in 2018, and three in 2017. (These deaths all followed legal abortions.) Since 1990, the annual number of deaths among women due to legal induced abortion has ranged from two to 12.

The annual number of reported deaths from induced abortions (legal and illegal) tended to be higher in the 1980s, when it ranged from nine to 16, and from 1972 to 1979, when it ranged from 13 to 63. One driver of the decline was the drop in deaths from illegal abortions. There were 39 deaths from illegal abortions in 1972, the last full year before Roe v. Wade. The total fell to 19 in 1973 and to single digits or zero every year after that. (The number of deaths from legal abortions has also declined since then, though with some slight variation over time.)

The number of deaths from induced abortions was considerably higher in the 1960s than afterward. For instance, there were 119 deaths from induced abortions in 1963 and 99 in 1965 , according to reports by the then-U.S. Department of Health, Education and Welfare, a precursor to the Department of Health and Human Services. The CDC is a division of Health and Human Services.

Note: This is an update of a post originally published May 27, 2022, and first updated June 24, 2022.

Support for legal abortion is widespread in many countries, especially in Europe

Nearly a year after roe’s demise, americans’ views of abortion access increasingly vary by where they live, by more than two-to-one, americans say medication abortion should be legal in their state, most latinos say democrats care about them and work hard for their vote, far fewer say so of gop, positive views of supreme court decline sharply following abortion ruling, most popular.

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A Study of the Impact of Strategic Human Resource Management on Organizational Resilience

Lingling Yuan

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Research trends in human resource management. A text-mining-based literature review

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