article review on decision making pdf

Decision Making: a Theoretical Review

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Published: 15 November 2021
Volume 56 , pages 609–629, ( 2022 )

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Matteo Morelli 1 ,
Maria Casagrande ORCID: orcid.org/0000-0002-4430-3367 2 &
Giuseppe Forte 1 , 3

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Decision-making is a crucial skill that has a central role in everyday life and is necessary for adaptation to the environment and autonomy. It is the ability to choose between two or more options, and it has been studied through several theoretical approaches and by different disciplines. In this overview article, we contend a theoretical review regarding most theorizing and research on decision-making. Specifically, we focused on different levels of analyses, including different theoretical approaches and neuropsychological aspects. Moreover, common methodological measures adopted to study decision-making were reported. This theoretical review emphasizes multiple levels of analysis and aims to summarize evidence regarding this fundamental human process. Although several aspects of the field are reported, more features of decision-making process remain uncertain and need to be clarified. Further experimental studies are necessary for understanding this process better and for integrating and refining the existing theories.

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Morelli, M., Casagrande, M. & Forte, G. Decision Making: a Theoretical Review. Integr. psych. behav. 56 , 609–629 (2022). https://doi.org/10.1007/s12124-021-09669-x

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Decision Making: a Theoretical Review

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1 Dipartimento di Psicologia, Università di Roma "Sapienza", Via dei Marsi. 78, 00185, Rome, Italy.
2 Dipartimento di Psicologia Dinamica, Clinica e Salute, Università di Roma "Sapienza", Via degli Apuli, 1, 00185, Rome, Italy. [email protected].
3 Dipartimento di Psicologia, Università di Roma "Sapienza", Via dei Marsi. 78, 00185, Rome, Italy. [email protected].
4 Body and Action Lab, IRCCS Fondazione Santa Lucia, Rome, Italy. [email protected].
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DOI: 10.1007/s12124-021-09669-x

Keywords: Decision making; Decision-making tasks; Decision-making theories; Neural correlates of decision making.

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Build a Corporate Culture That Works

There’s a widespread understanding that managing corporate culture is key to business success. Yet few companies articulate their culture in such a way that the words become an organizational reality that molds employee behavior as intended.

All too often a culture is described as a set of anodyne norms, principles, or values, which do not offer decision-makers guidance on how to make difficult choices when faced with conflicting but equally defensible courses of action.

The trick to making a desired culture come alive is to debate and articulate it using dilemmas. If you identify the tough dilemmas your employees routinely face and clearly state how they should be resolved—“In this company, when we come across this dilemma, we turn left”—then your desired culture will take root and influence the behavior of the team.

To develop a culture that works, follow six rules: Ground your culture in the dilemmas you are likely to confront, dilemma-test your values, communicate your values in colorful terms, hire people who fit, let culture drive strategy, and know when to pull back from a value statement.

Start by thinking about the dilemmas your people will face.

Idea in Brief

The problem.

There’s a widespread understanding that managing corporate culture is key to business success. Yet few companies articulate their corporate culture in such a way that the words become an organizational reality that molds employee behavior as intended.

What Usually Happens

How to fix it.

Follow six rules: Ground your culture in the dilemmas you are likely to confront, dilemma-test your values, communicate your values in colorful terms, hire people who fit, let culture drive strategy, and know when to pull back from a value.

At the beginning of my career, I worked for the health-care-software specialist HBOC. One day, a woman from human resources came into the cafeteria with a roll of tape and began sticking posters on the walls. They proclaimed in royal blue the company’s values: “Transparency, Respect, Integrity, Honesty.” The next day we received wallet-sized plastic cards with the same words and were asked to memorize them so that we could incorporate them into our actions. The following year, when management was indicted on 17 counts of conspiracy and fraud, we learned what the company’s values really were.

EM Erin Meyer is a professor at INSEAD, where she directs the executive education program Leading Across Borders and Cultures. She is the author of The Culture Map: Breaking Through the Invisible Boundaries of Global Business (PublicAffairs, 2014) and coauthor (with Reed Hastings) of No Rules Rules: Netflix and the Culture of Reinvention (Penguin, 2020). ErinMeyerINSEAD

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Open access
Published: 18 June 2024

Cognitive load theory in workplace-based learning from the viewpoint of nursing students: application of a path analysis

Shakiba Sadat Tabatabaee 1 ,
Sara Jambarsang 2 &
Fatemeh Keshmiri 3

BMC Medical Education volume 24 , Article number: 678 ( 2024 ) Cite this article

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The present study aimed to test the relationship between the components of the Cognitive Load Theory (CLT) including memory, intrinsic and extraneous cognitive load in workplace-based learning in a clinical setting, and decision-making skills of nursing students.

This study was conducted at Shahid Sadoughi University of Medical Sciences in 2021–2023. The participants were 151 nursing students who studied their apprenticeship courses in the teaching hospitals. The three basic components of the cognitive load model, including working memory, cognitive load, and decision-making as the outcome of learning, were investigated in this study. Wechsler’s computerized working memory test was used to evaluate working memory. Cognitive Load Inventory for Handoffs including nine questions in three categories of intrinsic cognitive load, extraneous cognitive load, and germane cognitive load was used. The clinical decision-making skills of the participants were evaluated using a 24-question inventory by Lowry et al. based on a 5-point scale. The path analysis of AMOS 22 software was used to examine the relationships between components and test the model.

In this study, the goodness of fit of the model based on the cognitive load theory was reported (GIF = 0.99, CFI = 0.99, RMSEA = 0.03). The results of regression analysis showed that the scores of decision-making skills in nursing students were significantly related to extraneous cognitive load scores ( p -value = 0.0001). Intrinsic cognitive load was significantly different from the point of view of nursing students in different academic years ( p = 0.0001).

The present results showed that the CLT in workplace-based learning has a goodness of fit with the components of memory, intrinsic cognitive load, extraneous cognitive load, and clinical decision-making skill as the key learning outcomes in nursing education. The results showed that the relationship between nursing students’ decision-making skills and extraneous cognitive load is stronger than its relationship with intrinsic cognitive load and memory Workplace-based learning programs in nursing that aim to improve students’ decision-making skills are suggested to manage extraneous cognitive load by incorporating cognitive load principles into the instructional design of clinical education.

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Introduction

Cognitive load was introduced as a key theory in medical education [ 1 ] This theory guides the components of human cognitive architecture concerning learning and education to create a correct understanding of the characteristics and conditions of education and learning [ 2 ].

Cognitive load theory (CLT)

The CLT was first proposed in the 1980s by John Sweller [ 3 ]. This theory explains learning according to three important aspects including types of memory (working and long-term memory), learning process, and forms of cognitive load that affect learning [ 4 ].

The cognitive architecture assumed by CLT includes long-term memory (LTM) and working memory (WM). The key subsystem of memory in the CLT is working memory [ 5 ].

Cognitive load

Cognitive load is defined as the load that a specific task imposes on the learner’s cognitive system [ 6 ]. In the CLT, three types of cognitive load are proposed, including intrinsic cognitive load (ICL), extraneous cognitive load (ECL), and germane cognitive load (GCL) [ 7 , 8 ]. ICL is related to the complexity of educational materials rather than their quantity [ 9 ]. ICL depends on several factors, including the individual’s skill, the number of information elements, and the degree of interaction of different elements of the tasks. ECL caused by the training format includes training strategies, training design, and teaching-learning methods [ 4 , 10 , 11 ]. GCL refers to the load imposed by the mental processes necessary for learning (such as the formation of schemata) [ 11 ]. Germane load means trying to build and modify learning schemata, which is mainly under the control of job components such as motivation, effort, and the learner’s metacognitive skills [ 7 ]. Also, the level of learner’s proficiency can moderate the ICL arising from the interaction of elements. This means that the availability and automaticity of the learner’s schemata can moderate intrinsic load [ 11 ].

Learning process

Education in medical science systems is a complex and multidimensional process that is affected by many factors [ 12 ]. In the process of clinical education, students need to learn several professional tasks and activities and apply them in the provision of health care services by simultaneously integrating a set of knowledge, skills, and behaviors [ 11 ]. These characteristics of clinical education can impose a high cognitive load on students and harm their effective learning [ 11 , 13 ].

CLT in health professions education

The CLT has emerged as one of the foremost models in educational psychology considered in different fields such as health professions education. The goal of CLT has been to improve learning at the individual student level in different environments including the classroom, and complex professional learning environments [ 14 ]. Sweller and colleagues showed there have been main developments in CLT and instructional design over the last 20 years. The ‘cognitive theory of multimedia learning’ focusing on the design of multimedia educational materials and the ‘four-component instructional design (4 C/ID)’ focusing on the design of whole-task courses and curricula have been built based on the CLT [ 15 ]. In addition, the CLT provides principles that are recommended to apply to the design of instructional messages and instructional units, such as lessons, written materials consisting of text and pictures, and educational multimedia (instructional animations, videos, simulations, games) [ 15 ].

The theoretical scope of the cognitive load has been expanded by including the physical environment as a key factor affecting cognitive load. Physical environments that evoke stress, emotions, and/or uncertainty raise new questions about how to deal with cognitive load. The questions require examining the human cognitive architecture of educational design in environments that are accompanied by uncertainty and stress [ 15 ]. Likewise, Paas et al. (2020) introduced variables affecting cognitive load and introduced factors including instructional design and learning environment as an effective factor that affects students’ learning process. They stated that the learning environment can affect cognitive load and suggested a way of managing it [ 5 ].

Advances in CLT have set the trends for future developments in different learning environments such as workplace-based learning, simulation, and games [ 15 ]. Most studies used the CLT principles in instructional design in simulation, virtual reality, and game settings in nursing education [ 1 , 16 , 17 , 18 ]. Yiin et al., (2023) indicated the multi-media interactive learning materials and an active learning mechanism reduced nursing students’ intrinsic and extrinsic cognitive load and encouraged the students to learn [ 19 ]. Takhdat et al., (2024) showed that mindfulness meditation practice optimizes cognitive load, and decreases the anxiety of nursing students in a simulation setting [ 20 ].

Clinical education in the workplace is defined as a main educational setting where students improve their competencies and prepare for their future careers. Sewell and colleagues (2019) in a BEME guide (Best Evidence in Medical Education guide) discussed cognitive load in workplace-based learning in the real environment [ 21 ]. The workplace-based learning in clinical education imposes high levels of cognitive load that negatively impact on learning of learners and their performances. Sewell et al. indicated the factors of, complex tasks, settings, and novice learners mostly predispose the students to high levels of cognitive load. They stated aspects of workplace environments contribute to extraneous load, and adversely impact capacity for engaging in tasks that enhance germane load and learning [ 15 ]. Further studies are recommended to understand the manner and the extent of the impact of cognitive load on different learning outcomes in various learning environments in systems of health professions education [ 1 , 16 , 17 ].

The present study aimed to test the relationship between the components of the Cognitive Load Theory (CLT) including memory, intrinsic and extraneous cognitive load in workplace-based learning, and decision-making skills of nursing students in clinical settings.

Materials and methods

This cross-sectional study was conducted in 2021–2023 at Shahid Sadoughi University of Medical Sciences, Yazd, Iran. In the present study, the path analysis was used to predict a defined theoretical model that posits hypothesized linear relations among variables and decreases to the solution of one or more multiple regression analyses.

The present university has conducted a four-year nursing degree curriculum. The students have participated in workplace-based learning in the clinical setting from the second semester. They contributed to care processes as team members from the third semester of the academic course. In the present nursing curriculum, there is no reasoning and decision-making training course. The decision-making skills have been learned by the students in the process of workplace-based learning in the clinical environment. The stages of experiential learning, including observation, practice and repetition, feedback, and self-reflection, have been implemented in the nursing clinical education program. In clinical education courses, the students have used study guides nursing flowcharts, and clinical guidelines.

Participants

Undergraduate nursing students of the faculties affiliated with Shahid Sadoughi University of Medical Sciences participated in this study. The inclusion criteria were nursing students who had completed at least six months of apprenticeship courses in their field in the hospital. Students with working experience as health technicians ( Behvarz ) were excluded from the study. This exclusion criterion aims to control for potential confounding variables that could influence the study’s outcomes, such as previous professional experience impacting cognitive load assessments and decision-making skills [ 22 , 23 ].

The rule of thumb is to have at least 10–15 observations per parameter (i.e., 10–15 cases for each independent variable and the dependent variable) to have reliable estimates of the model parameters [ 24 ]. Thus, a total of 151 eligible students were randomly selected in this study.

Data collection

To conduct the examination, the researcher explained the objectives of the research, the instruments of data collection, the duration of the examinations, and the confidentiality of data. The participants were asked to perform the Wechsler computerized working memory test and fill the Questionnaires of Cognitive Load Inventory for Handoffs and Clinical Decision-making in a calm environment and away from disturbing side factors. The informed consent form was completed by the students.

Study tools

Working memory measurement tool: Wechsler’s computerized working memory test was used to evaluate working (active) memory [ 25 , 26 ]. In this test, two sections of forward and backward recall of digits are used to measure the memory span. The total working memory score is obtained from the sum of the scores of the two parts of forward and backward recall with a maximum score is 28. For the correct evaluation of the subject, the soft table is used for the desired ages. In this software, the score of memory span (auditory and visual) is also provided. This score represents the number of items memorized by the examinee.

The cognitive Load Inventory for Handoffs (CLIH) was compiled by Yang et al., (2016) [ 27 ] to assess the cognitive load of students in their clinical education. The questionnaire includes 9 questions in three domains of ICL, ECL, and GCL which is based on a 10-point Likert. The validity of the tool was confirmed in the present study. The qualitative content validity of the Persian version of the questionnaire was confirmed from the viewpoints of 15 experts. To determine content validity quantitatively, two indices “Content Validity Ratio (CVR)” and “Content Validity Index (CVI)” were used. The findings of the quantitative content validity assessment indicated that the CVR for all items was higher than the minimum acceptable value (= 0.49), and the CVI values of all items were above 0.79. According to the indices, all items were kept in the questionnaire. S-CVI/Ave was 0.94, which was desirable. The internal consistency of the tool was reported as Cronbach’s alpha coefficient = 0.86.

Clinical decision-making as a learning outcome of nursing students in clinical education was evaluated using the 24-item questionnaire designed by Lauri et al. (2001) which is based on a 5-point scale [ 12 ]. The reliability and validity were confirmed in the Karimi et al. study (2013) (Cronbach’s alpha coefficient of intrinsic consistency = 0.8) [ 28 ].

Data analysis

Demographic information of the participants (including gender, age, level of education, and the last externship/internship period of the students) was collected. Descriptive statistics (including frequency percentage, mean, and standard deviation) and analytical statistics (ANOVA) were used to investigate the variables. SPSS statistical software (Ver. 24) was used for data analysis.

This study employed path analysis as the primary statistical analysis method due to its ability to examine the relationships between multiple variables, including the direct and indirect effects of predictor variables on the outcome variable. Specifically, path analysis was used to investigate the relationships between memory, internal and external cognitive load, and decision-making skill, as well as the indirect effects of these variables on learning outcomes. Moreover, path analysis is suited for examining the relationships among the variables in this study due to the capability of path analysis to handle complex models and multiple relationships simultaneously. The use of path analysis was further justified by the need to examine the causal relationships between variables, as well as to account for measurement error and unexplained variance in the data. Path analysis allows for the estimation of standardized regression coefficients, which can be used to interpret the magnitude and direction of the relationships between variables.

In terms of model evaluation, this study employed several indices to assess the goodness-of-fit of the proposed model. The goodness-of-fit index (GFI) was also used to evaluate the model’s fit relative to a baseline model, with a value of 0.95 or higher indicating a good fit [ 29 ]. In addition, acceptable levels of indices of the path analysis include Adjusted Goodness-of-Fit Index (AGFI) > 0.8, Tucker-Lewis Index (TLI) > 0.9, the Incremental Fit Index (IFI) > 0.8. Regarding the Comparative Fit Index (CFI) with a value of greater than 0.90 is very good fit, 0.80 to 0.89 is adequate but marginal fit, 0.60 to 0.79 is poor fit, a and lower than 0.60 very poor fit. Finally, the root mean square error of approximation (RMSEA) was used to evaluate the model’s fit to the data, with a value of 0.05 or less indicating a good fit [ 30 ]. These results indicate that the proposed model provided an adequate representation of the relationships among the variables studied. In the present study, AMOS 22 software was used to assess the fitness of this model.

In total, 151 nursing students participated in this study, 77 of them (51%) were women and 74 (49%) were men. The mean age of the participants was 21.97 ± 2.20. The demographic information of the participants is shown in Table 1 .

The mean score of decision-making of nursing students was 78.37 ± 11.30 and the mean score of cognitive load perceived by students in the workplace-based learning process of clinical setting was 45.26 ± 8.84. Table 2 shows the mean score of the students in the studied variables.

The results of regression analysis showed that the students’ scores of nursing students in decision-making skills were significantly related to the ECL scores ( P = 0.0001). By increasing one ECL score, the score of students’ clinical decision-making skill increased by 1.2.

The mean scores of ICL and ECL of the students according to their academic year are reported in Table 3 . ANOVA showed that ICL was significantly different from the point of view of nursing students in different academic years ( P = 0.0001). The results of the Bonferroni test showed that ICL in novice (second-year) students was significantly lower than in third-year ( P = 0.0001) and fourth-year students ( P = 0.004). Figure 1 illustrate the path analysis model of CLT in the workplace-based leaning. Table 4 show a report of indices of goodness-of-fit in the model.

Path analysis model: standardized coefficient estimates

ICL: Intrinsic cognitive load, ECL: Extraneous cognitive load

The current study reported a statistically significant fit for the proposed path analysis model indicating a good fit in the data collected from the nursing students in the workplace-based learning at clinical setting.

The development of clinical decision-making skills is a main competency of nursing students in clinical education courses. Learning the decision-making skill is considered a complex and multi-dimensional process that is influenced by various factors for instance personal features, task experience, and situational awareness ability [ 22 ]. Moreover, educational factors such as instructional design, learning environments, and teaching methods direct the cognitive load and learning process of students [ 12 , 31 ]. The present results showed that nursing students’ decision-making skills have a significant positive relationship the capacity of the working memory of learners and ECL in workplace-based learning environments. In line with our results, the findings of studies confirmed management of ECL that depended on the characteristics of the instructional material, the instructional design, and the prior knowledge of learners in the process of clinical education have a positive relationship with learning [ 5 , 21 , 23 ]. The effect of cognitive load as a mediating relationship on clinical reasoning as the key outcome of learning was shown in the Jung et al. study (2022) [ 32 ]. In a review, Josephsen et al. (2015) showed that there is a positive relationship between the cognitive architecture of learners and educational design in nursing. Their results indicated that learners must be aware of cognitive architecture and educational processes in nursing to manage cognitive load and effective learning [ 16 ].

The present results showed that the decision-making scores of the nursing students had a significant positive relationship with ECL in workplace-bead learning. The students have experienced the experiential learning process in clinical nursing education. They learned through observing, exercising, receiving feedback, and reflecting in action and on action at the workplace-based learning in the clinical setting. In addition, nursing students used supportive resources such as a nursing flowchart, a study guide, and structured constructive feedback in clinical education. The use of CLT principles in the instructional design of workplace-based learning of nursing clinical education effects on the ECL. Many learning tasks, especially complex clinical activities, require memorizing and applying a large amount of information [ 11 ]. According to the CLT, the educational environment provides a trigger to use the information stored in LTM to determine the appropriate action in the environment according to the environmental-and-organizing linking principle. Moreover, specialized performance is developed through the creation of a large number of more complex schemata by combining elements consisting of lower-level schemata with higher-level schemata [ 5 ]. The schemata facilitate the decision-making process. The significant relationship between the ECL and learning has also been confirmed in the study of Sawicka et al. (2008) [ 9 ]. The application of strategies for ECL management is recommended by Sawicka et al. The tailored strategies with the workplace-based education were conducted in the clinical setting. These strategies include presenting educational materials from simple to complex and presenting familiar examples in the experiential learning process in clinical setting. The students were experienced the nursing care plan form simple cases to complex cases. The supplementary questions and diverse assignments were conducted in the clinical education by students. They experienced self-explanatory and supporting information in the feedback and reflection process. The use of the strategies in the clinical education of the nursing students in workplace-based learning may effect on our findings. Similarly, Skulmowski et al., (2022) acknowledged the use of aspects of constructive alignment, a strategy to balance the cognitive load and an approach of fostering deep forms of learning improved the learning outcomes [ 33 ].

In the CLT, the features of working memory including its capacity and time limitations were introduced as a key component that plays an important role in learning. This issue is emphasized in cognitive models [ 5 , 34 ]. The present results showed that the relationship between memory and ICL is stronger than ECL. Kilic et al. (2010) model indicated that working memory plays an effective role in providing information necessary for complex cognitive activities such as learning and clinical reasoning [ 34 ]. So, if the learning material is too difficult, the ICL imposed on learners may exceed their working memory capacity and hinder learning [ 9 ]. In line with our results, the relationship of working memory with ICL was stranger than ECL. Sawicka (2008) stated that the insufficiency of working memory resources to expand schemata hinders learning [ 9 ].

The present results showed that the fit of the model was favorable by considering working memory scores, cognitive load, and learning, but no significant relationship was observed between working memory and decision-making scores. Also, no significant relationship between ICL and learning was observed in the present study. In line with our results, Szulewski et al., (2021) presented a new model for medical education systems based on CLT. They stated the relationship between the working memory of healthcare workers cannot be discussed directly in the model. They expressed this as a limitation of their model and acknowledged that the capacity of working memory in complex medical education systems is affected by stress, emotions, and uncertainties, which can affect the performance of healthcare workers [ 14 ]. Although the significant relationship between the components was not approved in the present study, the good fit of the proposed path analysis model, indicated that these components interact with each other and require consideration as a coherent structure in instructional design of the workplace-based learning by planners.

Emotions, stress, and uncertainty are integrated with the learning process and environment in the educational systems of health professions. The educational systems of health professions integrate emotions, stress, and uncertainty into the learning process and environment. According to Sweller, emotions that are considered undesirable for learning result in extraneous load that can be reduced by preventing them. If emotion, stress, and uncertainty are seen as an integral element of the task that learners require to learn, they contribute to intrinsic cognitive load and must be dealt with in another way. Therefore, it is necessary to consider multi-faceted planning by using different components and systematically examining different aspects of cognitive load before formulating educational designs for workplace-based learning in the clinical setting [ 5 ].

Garvey et al. study (2017) introduced a model in which, in addition to the cognitive load components, the individual maturity component based on the years of education was also included in the model [ 35 ]. In the present study, individual maturity was considered in different academic years. The present results showed that there is a significant relationship between the learning maturity of individuals and ICL components. ICL is related to the complexity caused by training and depends on factors such as the individual’s skill, the number of information elements, and the degree of interaction of elements in the learning process. Our findings indicated the ICL of the second-year students was significantly lower compared to the third-year and the fourth-year. The results can be due to less work experience in the hospital, the smaller amount of material learned, and dealing with the limited clinical complexities of the students in the second year. Sewell’s results confirmed a negative relationship between GCL and ICL with the level of experience and performance of students [ 21 ]. These results were also aligned with the present results. Our results are in contrast to Schlairet’s findings (2015) which indicated that a negative relationship between the performance of novice nursing students and cognitive load was observed, although this relationship was not significant [ 36 ]. The difference in the level of students and the difference in the measured learning outcome (decision-making skills versus performance) and considering the cognitive load score without separating ICL and ECL can affect the results.

The results showed that the current model does not have a good fit considering the GCL. The current limitation can be due to measuring the GCL using only one question in CLIH [ 27 ]. Measuring the GCL as a mental process of learning is difficult and requires the measurement of supporting components such as motivation, effort, and metacognitive skills [ 7 ]. In a meta-analysis, Lapierre (2022) found that cognitive load measurement is one of the concerns of studies in the field of CLT. He stated that appropriate tools and the use of self-expression are among the concerns of studies in this field [ 1 ]. Therefore, it is recommended to use different tools to measure the desired cognitive load component in future studies [ 5 , 17 ]. Moreover, it is suggested that influential components such as factors affecting the GCL, learning maturity, and educational strategies should be taken into consideration in future studies.

CLT is a key theory in the purposeful guidance of the process of education, which can guide the educational processes to more effective learning in medical science education systems. The current results showed that CLT had a good fit with the components of memory, ICL, ECL, and clinical decision-making as the key learning outcomes in workplace-based learning in clinical settings. The results showed that the relationship between nursing students’ decision-making skills and extraneous cognitive load is stronger than its relationship with intrinsic cognitive load and memory. Workplace-based learning programs in nursing that aim to improve students’ decision-making skills are suggested to manage extraneous cognitive load by incorporating cognitive load principles into the instructional design of clinical education.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to the confidentiality of the data of participants but are available from the corresponding author at reasonable request.

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Acknowledgements

The authors appreciate the cooperation of Amir Houshang Mehrparvar. We would like to thank all the participants for their contribution.

The Shahid Sadoughi University of Medical Sciences, Yazd, Iran funded this project (ID: 16139). The grant supported the data collection process. The funders had no role in the design of the study and collection, analysis, interpretation of data, or preparation of the manuscript. The report of the study’s findings is sent by the authors to the funder at the end of the study.

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Shakiba Sadat Tabatabaee

Center for Healthcare Data Modeling, Department of Biostatistics and Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran

Sara Jambarsang

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F.K. and SH.T. conceptualized and designed the study and SH.T. collected the data. S.J analyzed the data. F.K. and SH.T wrote the main manuscript text. The authors have met the criteria for authorship and had a role in preparing the manuscript. Also, all authors approved the final manuscript.

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Tabatabaee, S.S., Jambarsang, S. & Keshmiri, F. Cognitive load theory in workplace-based learning from the viewpoint of nursing students: application of a path analysis. BMC Med Educ 24 , 678 (2024). https://doi.org/10.1186/s12909-024-05664-z

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What is decision making?

Signpost with three blank signs on sky backgrounds

Decisions, decisions. When was the last time you struggled with a choice? Maybe it was this morning, when you decided to hit the snooze button—again. Perhaps it was at a restaurant, with a miles-long menu and the server standing over you. Or maybe it was when you left your closet in a shambles after trying on seven different outfits before a big presentation. Often, making a decision—even a seemingly simple one—can be difficult. And people will go to great lengths—and pay serious sums of money—to avoid having to make a choice. The expensive tasting menu at the restaurant, for example. Or limiting your closet choices to black turtlenecks, à la Steve Jobs.

Get to know and directly engage with senior McKinsey experts on decision making

Aaron De Smet is a senior partner in McKinsey’s New Jersey office, Eileen Kelly Rinaudo is McKinsey’s global director of advancing women executives and is based in the New York office, Frithjof Lund is a senior partner in the Oslo office, and Leigh Weiss is a senior adviser in the Boston office.

If you’ve ever wrestled with a decision at work, you’re definitely not alone. According to McKinsey research, executives spend a significant portion of their time— nearly 40 percent , on average—making decisions. Worse, they believe most of that time is poorly used. People struggle with decisions so much so that we actually get exhausted from having to decide too much, a phenomenon called decision fatigue.

But decision fatigue isn’t the only cost of ineffective decision making. According to a McKinsey survey of more than 1,200 global business leaders, inefficient decision making costs a typical Fortune 500 company 530,000 days of managers’ time each year, equivalent to about $250 million in annual wages. That’s a lot of turtlenecks.

How can business leaders ease the burden of decision making and put this time and money to better use? Read on to learn the ins and outs of smart decision making—and how to put it to work.

Learn more about our People & Organizational Performance Practice .

How can organizations untangle ineffective decision-making processes?

McKinsey research has shown that agile is the ultimate solution for many organizations looking to streamline their decision making . Agile organizations are more likely to put decision making in the right hands, are faster at reacting to (or anticipating) shifts in the business environment, and often attract top talent who prefer working at companies with greater empowerment and fewer layers of management.

For organizations looking to become more agile, it’s possible to quickly boost decision-making efficiency by categorizing the type of decision to be made and adjusting the approach accordingly. In the next section, we review three types of decision making and how to optimize the process for each.

What are three keys to faster, better decisions?

Business leaders today have access to more sophisticated data than ever before. But it hasn’t necessarily made decision making any easier. For one thing, organizational dynamics—such as unclear roles, overreliance on consensus, and death by committee—can get in the way of straightforward decision making. And more data often means more decisions to be taken, which can become too much for one person, team, or department. This can make it more difficult for leaders to cleanly delegate, which in turn can lead to a decline in productivity.

Leaders are growing increasingly frustrated with broken decision-making processes, slow deliberations, and uneven decision-making outcomes. Fewer than half of the 1,200 respondents of a McKinsey survey report that decisions are timely, and 61 percent say that at least half the time they spend making decisions is ineffective.

What’s the solution? According to McKinsey research, effective solutions center around categorizing decision types and organizing different processes to support each type. Further, each decision category should be assigned its own practice—stimulating debate, for example, or empowering employees—to yield improvements in effectiveness.

Here are the three decision categories that matter most to senior leaders, and the standout practice that makes the biggest difference for each type of decision.

Big-bet decisions are infrequent but high risk, such as acquisitions. These decisions carry the potential to shape the future of the company, and as a result are generally made by top leaders and the board. Spurring productive debate by assigning someone to argue the case for and against a potential decision can improve big-bet decision making.
Cross-cutting decisions, such as pricing, can be frequent and high risk. These are usually made by business unit heads, in cross-functional forums as part of a collaborative process. These types of decisions can be improved by doubling down on process refinement. The ideal process should be one that helps clarify objectives, measures, and targets.
Delegated decisions are frequent but low risk and are handled by an individual or working team with some input from others. Delegated decision making can be improved by ensuring that the responsibility for the decision is firmly in the hands of those closest to the work. This approach also enhances engagement and accountability.

In addition, business leaders can take the following four actions to help sustain rapid decision making :

Focus on the game-changing decisions, ones that will help an organization create value and serve its purpose.
Convene only necessary meetings, and eliminate lengthy reports. Turn unnecessary meetings into emails, and watch productivity bloom. For necessary meetings, provide short, well-prepared prereads to aid in decision making.
Clarify the roles of decision makers and other voices. Who has a vote, and who has a voice?
Push decision-making authority to the front line—and tolerate mistakes.

Circular, white maze filled with white semicircles.

Introducing McKinsey Explainers : Direct answers to complex questions

How can business leaders effectively delegate decision making.

Business is more complex and dynamic than ever, meaning business leaders are faced with needing to make more decisions in less time. Decision making takes up an inordinate amount of management’s time—up to 70 percent for some executives—which leads to inefficiencies and opportunity costs.

As discussed above, organizations should treat different types of decisions differently . Decisions should be classified according to their frequency, risk, and importance. Delegated decisions are the most mysterious for many organizations: they are the most frequent, and yet the least understood. Only about a quarter of survey respondents report that their organizations make high-quality and speedy delegated decisions. And yet delegated decisions, because they happen so often, can have a big impact on organizational culture.

The key to better delegated decisions is to empower employees by giving them the authority and confidence to act. That means not simply telling employees which decisions they can or can’t make; it means giving employees the tools they need to make high-quality decisions and the right level of guidance as they do so.

Here’s how to support delegation and employee empowerment:

Ensure that your organization has a well-defined, universally understood strategy. When the strategic intent of an organization is clear, empowerment is much easier because it allows teams to pull in the same direction.
Clearly define roles and responsibilities. At the foundation of all empowerment efforts is a clear understanding of who is responsible for what, including who has input and who doesn’t.
Invest in capability building (and coaching) up front. To help managers spend meaningful coaching time, organizations should also invest in managers’ leadership skills.
Build an empowerment-oriented culture. Leaders should role model mindsets that promote empowerment, and managers should build the coaching skills they want to see. Managers and employees, in particular, will need to get comfortable with failure as a necessary step to success.
Decide when to get involved. Managers should spend effort up front to decide what is worth their focused attention. They should know when it’s appropriate to provide close guidance and when not to.

How can you guard against bias in decision making?

Cognitive bias is real. We all fall prey, no matter how we try to guard ourselves against it. And cognitive and organizational bias undermines good decision making, whether you’re choosing what to have for lunch or whether to put in a bid to acquire another company.

Here are some of the most common cognitive biases and strategies for how to avoid them:

Confirmation bias. Often, when we already believe something, our minds seek out information to support that belief—whether or not it is actually true. Confirmation bias involves overweighting evidence that supports our belief, underweighting evidence against our belief, or even failing to search impartially for evidence in the first place. Confirmation bias is one of the most common traps organizational decision makers fall into. One famous—and painful—example of confirmation bias is when Blockbuster passed up the opportunity to buy a fledgling Netflix for $50 million in 2000. (Actually, that’s putting it politely. Netflix executives remember being “laughed out” of Blockbuster’s offices.) Fresh off the dot-com bubble burst of 2000, Blockbuster executives likely concluded that Netflix had approached them out of desperation—not that Netflix actually had a baby unicorn on its hands.
Herd mentality. First observed by Charles Mackay in his 1841 study of crowd psychology, herd mentality happens when information that’s available to the group is determined to be more useful than privately held knowledge. Individuals buy into this bias because there’s safety in the herd. But ignoring competing viewpoints might ultimately be costly. To counter this, try a teardown exercise , wherein two teams use scenarios, advanced analytics, and role-playing to identify how a herd might react to a decision, and to ensure they can refute public perceptions.
Sunk-cost fallacy. Executives frequently hold onto underperforming business units or projects because of emotional or legacy attachment . Equally, business leaders hate shutting projects down . This, researchers say, is due to the ingrained belief that if everyone works hard enough, anything can be turned into gold. McKinsey research indicates two techniques for understanding when to hold on and when to let go. First, change the burden of proof from why an asset should be cut to why it should be retained. Next, categorize business investments according to whether they should be grown, maintained, or disposed of—and follow clearly differentiated investment rules for each group.
Ignoring unpleasant information. Researchers call this the “ostrich effect”—when people figuratively bury their heads in the sand , ignoring information that will make their lives more difficult. One study, for example, found that investors were more likely to check the value of their portfolios when the markets overall were rising, and less likely to do so when the markets were flat or falling. One way to help get around this is to engage in a readout process, where individuals or teams summarize discussions as they happen. This increases the likelihood that everyone leaves a meeting with the same understanding of what was said.
Halo effect. Important personal and professional choices are frequently affected by people’s tendency to make specific judgments based on general impressions . Humans are tempted to use simple mental frames to understand complicated ideas, which means we frequently draw conclusions faster than we should. The halo effect is particularly common in hiring decisions. To avoid this bias, structured interviews can help mitigate the essentializing tendency. When candidates are measured against indicators, intuition is less likely to play a role.

For more common biases and how to beat them, check out McKinsey’s Bias Busters Collection .

Learn more about Strategy & Corporate Finance consulting at McKinsey—and check out job opportunities related to decision making if you’re interested in working at McKinsey.

Articles referenced include:

“ Bias busters: When the crowd isn’t necessarily wise ,” McKinsey Quarterly , May 23, 2022, Eileen Kelly Rinaudo , Tim Koller , and Derek Schatz
“ Boards and decision making ,” April 8, 2021, Aaron De Smet , Frithjof Lund , Suzanne Nimocks, and Leigh Weiss
“ To unlock better decision making, plan better meetings ,” November 9, 2020, Aaron De Smet , Simon London, and Leigh Weiss
“ Reimagine decision making to improve speed and quality ,” September 14, 2020, Julie Hughes , J. R. Maxwell , and Leigh Weiss
“ For smarter decisions, empower your employees ,” September 9, 2020, Aaron De Smet , Caitlin Hewes, and Leigh Weiss
“ Bias busters: Lifting your head from the sand ,” McKinsey Quarterly , August 18, 2020, Eileen Kelly Rinaudo
“ Decision making in uncertain times ,” March 24, 2020, Andrea Alexander, Aaron De Smet , and Leigh Weiss
“ Bias busters: Avoiding snap judgments ,” McKinsey Quarterly , November 6, 2019, Tim Koller , Dan Lovallo, and Phil Rosenzweig
“ Three keys to faster, better decisions ,” McKinsey Quarterly , May 1, 2019, Aaron De Smet , Gregor Jost , and Leigh Weiss
“ Decision making in the age of urgency ,” April 30, 2019, Iskandar Aminov, Aaron De Smet , Gregor Jost , and David Mendelsohn
“ Bias busters: Pruning projects proactively ,” McKinsey Quarterly , February 6, 2019, Tim Koller , Dan Lovallo, and Zane Williams
“ Decision making in your organization: Cutting through the clutter ,” McKinsey Quarterly , January 16, 2018, Aaron De Smet , Simon London, and Leigh Weiss
“ Untangling your organization’s decision making ,” McKinsey Quarterly , June 21, 2017, Aaron De Smet , Gerald Lackey, and Leigh Weiss
“ Are you ready to decide? ,” McKinsey Quarterly , April 1, 2015, Philip Meissner, Olivier Sibony, and Torsten Wulf.

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Political dimensions...

Political dimensions of misinformation, trust, and vaccine confidence in a digital age

How are social media influencing vaccination read the full collection.

Related content
Peer review
Luisa Enria , associate professor in anthropology 1 ,
Harriet Dwyer , doctoral student 1 ,
Mark Marchant , doctoral student 1 ,
Nadine Beckmann , associate professor in social science 1 ,
Megan Schmidt-Sane , research fellow in anthropology 2 ,
Abu Conteh , senior research officer in urban health 3 ,
Anthony Mansaray , doctoral student 1 ,
Alhaji N’Jai , associate professor of medicine and infectious disease 4
1 London School of Hygiene and Tropical Medicine, London UK
2 Institute of Development Studies, University of Sussex, Brighton, UK
3 Sierra Leone Urban Research Centre, Freetown, Sierra Leone
4 College of Medicine and Allied Health Sciences, University of Sierra Leone, Freetown, Sierra Leone
Correspondence to: L Enria luisa.enria2{at}lshtm.ac.uk

Global health leaders often dismiss politics as antithetical to the aims of public health, but Luisa Enria and colleagues argue that political analysis can offer new ways to build trust in vaccination in the context of growing online misinformation

In April 2020, as covid-19 spread across the world, the director general of the World Health Organization, Tedros Adhanom Ghebreyesus, appealed to the global community: “please do not politicise this virus.” Later that year, as covid-19 vaccines became available, scientists expressed concerns about the dangers of immunisation becoming political. This desire to keep politics separate from health is “misguided,” as the two are “inexorably intertwined.” 1 Rather than wish politics away, understanding exactly how it shapes health outcomes can help to identify new ways to tackle global health challenges. Vaccine confidence is a prime example of this.

Vaccine confidence refers to trust in the safety or efficacy of vaccines, encompassing trust in the vaccine (the product), the vaccinator (the service provider), and those who make decisions about vaccine provision (the policy maker). 2 Conversely, vaccine hesitancy refers to “refusal, delay or acceptance with doubt about vaccine usefulness.” 3 In 2019 WHO declared vaccine hesitancy to be among the top global health challenges. In recent years, mistrust in vaccination has come centre stage in the aftermath of resurgent outbreaks of preventable diseases and through the rise of organised anti-vaccination movements that became particularly visible during the covid-19 pandemic. These concerns are further heightened in the context of what many are calling an “infodemic”—that is, the drastic increase in circulation of information, including misleading and false news, that has accompanied the rise of social media and hyperconnectivity in the digital age. 4

Public health approaches to these challenges have tended to focus on individual or cognitive drivers of decision making around vaccination. Resulting strategies such as “debunking” assume that the problem is poor or insufficient information. 5 However, by focusing on individual exposure to misinformation, such approaches may miss how people’s collective experiences, as well as broader societal, historical, and political contexts, shape how they interpret different types of information and make decisions about immunisation. Growing evidence in the social sciences shows that engagements with vaccination are “socially and politically embedded processes,” requiring that we widen our lens beyond the individual. 5

In this article, we argue that a political analysis can help us to view vaccine confidence in context and develop more holistic approaches to tackling mistrust. Such analysis highlights both the direct and indirect political factors that may influence perspectives of and decisions about vaccination. These include, for example, experiences of marginalisation and (mis)trust in government institutions, as well as political decisions about levels of investment in health services and the explicit mobilisation of voters around the question of vaccination ( box 1 ). We illustrate this approach through a discussion of trust and distrust in vaccination in the context of online misinformation.

Political analysis of vaccine confidence and hesitancy

Although locally situated, a political analysis of vaccine hesitancy might consider how the following factors may influence vaccine confidence and uptake.

Indirect political factors

Experiences of historical exclusion—on the basis of ethnic identity, religion, geographical location, gender, and so on (or some combination of these factors)

Contemporary experiences of exclusion or marginality

Trust in government institutions

Attitudes towards and definitions of different kinds of authority (eg, authoritative information, authoritative public health/medical experts, public authorities, religious authorities, external global health actors)

Political decisions about funding of the health sector that influences everyday experiences of healthcare

Direct political factors

Political mobilisation specifically around the topic of vaccines by interest groups

Political mobilisation specifically around the topic of vaccines by politicians

Partisan political affiliation

Vaccine confidence in the digital age

The worldwide decline in vaccine confidence in recent years can be partly associated with the rapid expansion of social media. 6 Delays in and refusals of vaccination have been shown to be “more frequent in people who reported the internet as their main source of information,” and negative information about vaccines spreads faster online than positive information. 7 These trends are concerning, but assuming that misinformation is the sole explanation for vaccine refusal can be risky. Although evidence shows that online misinformation correlates with reduced vaccination intention, 8 intentions do not always predict behaviour. 9 Most importantly, focusing on misinformation may lead to characterisations of vaccine hesitancy as primarily a problem of insufficient or incorrect information. 10 Social science research has shown that hesitancy rarely reflects knowledge deficits and often even has little to do with the vaccine itself, rather reflecting problems of mistrust in experts, institutions, and authorities. 11 12 A contextual understanding of people’s offline experiences can offer insights into how people engage with online (mis)information and how this in turn shapes their views on vaccination.

Vaccine hesitancy as a commentary on mistrust

A political analysis helps to situate vaccines in this broader context, considering indirect political factors such as citizens’ relationships to their governments and how vaccination becomes implicated in wider contestations of political authority. Studies have consistently shown that lower trust in government is linked to lower vaccination intentions. 13 14 This has been the case since the introduction of the first vaccines in 19th century Britain, which sparked widespread working class protests around poor working and living conditions. 15 Similarly, refusals of smallpox vaccination became part of challenges to colonial rule in India. 16

Content analyses of concerns around vaccines similarly show that they often reflect anxieties about the motives of government and public health officials. During the Ebola vaccine trials in Sierra Leone, for example, fears circulated that the novel vaccine may be a ploy to decimate the population and steal blood for westerners’ use, echoing violent and extractive colonial pasts. 17 Similar narratives re-emerged and spread rapidly through social media that the covid-19 vaccine was intended to “kill people slowly in Africa.” 18 Concerns surrounding vaccination can, in other words, be read as commentaries of mistrust that go far beyond immunisation.

Such mistrust is also rarely unjustified. In 2003 boycotts of the polio vaccine in northern Nigeria were linked, among other factors, to memories of the 1996 Pfizer trial of a meningitis drug that resulted in high profile lawsuits around the company’s failure to obtain informed consent. 19 Mistrust in the federal government’s collaboration with western pharmaceutical companies was cited in subsequent vaccination campaigns as a reason for refusal. 20 Similarly, in the US, lower rates of vaccine confidence among Black, indigenous, and other communities have been connected to mistrust in the government and experiences of structural racism and state violence. Black American participants in a study about the flu vaccine cited the historical legacy of racist scientific experiments for not trusting a “government vaccine.” 21 For many Black Americans, medical encounters continue to be marked by experiences of disrespect and discrimination. 22 Such experiences affect vaccine uptake. In Sierra Leone, rural mothers reflected on humiliating previous experiences at health centres, inadequate care, and excessive costs as reasons why they were discouraged from taking their children to be vaccinated. 23 This kind of structural violence poses significant barriers to accessing vaccination and makes translating health information into action difficult. Simply classifying these groups as vaccine hesitant can hide the broader processes of marginalisation that erode trust in government and health providers, giving legitimate reasons for being apprehensive.

Labelling minoritised groups as “vaccine hesitant” has been shown to reinforce exclusion. Research on ultra-orthodox Jewish communities shows that decisions about vaccination are complex and rarely lead to blanket refusal but that official discourses focused on hesitancy served to bolster “antisemitic representations of Jews as public (health) risks,” paying little attention to the ongoing “crisis of confidence” in these long neglected communities. 24

Experiences of exclusion, memories of historical oppression, and contemporary experiences of structural violence, underfunding of healthcare, and rising inequality therefore shape attitudes to vaccines and filter how people engage with information they receive about them. Whereas misinformation is increasingly global, how people make sense of the information they receive remains local.

Direct mobilisation around vaccines

In recent years we have also seen more direct efforts to bring vaccination into political discourse, as politicians and interest groups increasingly explicitly mobilise their electorates and membership around the topic of vaccines. Doubts around vaccination have been central to the political campaigns of “populist” parties and politicians. Gaining momentum with the rise in social media, populist politics, broadly defined, relies on a contrast between “the people” and “the political establishment,” as these movements capitalise on feelings of mistrust and disenfranchisement. 25 Parties such as Italy’s Five Star Movement explicitly expressed concerns about the posited connection between measles, mumps, and rubella vaccines and autism, 26 before changing their position during the covid-19 pandemic. Similarly, former President Magufuli of Tanzania stated that he would not acquire covid-19 vaccines as these may have been “manipulated by imperialists to harm Tanzanians” as a key component of his political platform. 27

The rising appeal of populist politics has been accompanied by increased polarisation. Affiliation to political parties has been shown to be a predictor of vaccination intention, as has exposure to different kinds of media. 28 29 A study in 2019 showed that the percentage of people who voted for a populist party in the 2014 European elections was positively associated with the number of people who believed that “vaccines are not safe or important.” 26 Vaccines can therefore be a polarising topic, but how they become polarising depends on context. In western Europe, for example, leaning to the political left or right did not matter as much in determining attitudes to vaccines as did holding an “anti-elitist worldview.” 30

Another example of direct political mobilisation around vaccination is the anti-vaccination (anti-vax) movement. This movement represents a minority opinion, yet it has been shown to fuel misinformation online, with the potential to influence a broader constituency of people who may have legitimate concerns or be undecided. Prominent anti-vaxxers have also openly supported political campaigns and received support from politicians, gaining power and visibility. 31

Rebuilding trust in the digital age

The challenge of vaccine hesitancy has given rise to a range of efforts to tackle it. Efforts that focus solely on debunking misinformation or on providing more information to individuals have been shown to be ineffective in tackling the underlying causes of mistrust. During the west African Ebola epidemic, researchers highlighted that interventions aimed at correcting “misconceptions” around the disease failed to engage with the plethora of reasons why many people feared reporting loved ones to the hospital. 32 Conversely, community engagement approaches that focus on two way dialogue and directly engage with the diversity of people’s experiences and opinions have been shown to increase trust and participation, including in the context of vaccination. 33 34 A political analysis can contribute to the efforts of people involved in combating mistrust in vaccines in several ways.

Researchers: political analysis to understand power in (online) context

Studies on the dynamics of (mis)trust in vaccines and public health emergency management have shown that understanding who is trusted and who has the legitimacy to speak on matters of public concern can improve the success of community engagement efforts. 35 36 Political analysis can help in observing patterns of (mis)trust and identifying trusted sources of information. In Sierra Leone, this helped vaccinators to diversify their community engagement strategies to reflect varied levels of trust across different groups in heterogeneous communities. Although this work has been done primarily offline, efforts to engage people online could benefit from deeper social network and stakeholder analyses in digital spaces. Dynamic and long term social science analysis of online content is needed to identify the political context and drivers of mistrust. This must be complemented with offline studies of perceptions to avoid the risk of ignoring populations who are not connected and to understand how online information affects offline behaviour.

Public health practitioners and healthcare workers: tackling political roots of mistrust

For public health practitioners, a political analysis of vaccine hesitancy can help to situate and tackle the challenge. For example, whereas the problem for a health worker may be when a mother refuses to vaccinate her child, the solution may not lie in the mother’s improved information or the health worker’s persuasion skills. It may lie instead in the need to transform the institutions that generated her mistrust in vaccines. Political analysis directs us to who needs to change and how. This analysis can furthermore support health workers tasked with community engagement to broaden their dialogue beyond immunisation to directly discuss the underlying concerns facing their patients and communities. Similarly, being cognisant of political context can help in reconsidering how public health campaigns are run. Evidence has shown the negative consequences on trust of militarised outbreak control and vaccination efforts. 36

This reframing may also require reflexive practice within the health system on how to become more trustworthy. A study on disparities in patient safety in the US by racial and ethnic groups concluded that “health care organizations and systems will need to reflect on their role in creating the conditions in which patients’ beliefs about their trustworthiness are formed.” 37 Limited evidence exists on best practice for such reflexive approaches, but a community engagement intervention in Sierra Leone that included a frank dialogue around shortcomings of health systems yielded positive outcomes. 23

Civil society: building multisectoral coalitions for long term trustworthiness

Framing low confidence in vaccines within wider political dynamics will build the case for a shared responsibility that goes beyond the health system. Public health efforts to strengthen confidence in vaccines would benefit from taking a multi-sectoral approach, joining current efforts to strengthen democracy and trust in institutions. In a climate of polarisation and political tensions around vaccines, two way dialogue about controversial political topics has been shown to change minds by reducing the threat perception of the opposing side’s arguments and providing “affirmation and mutual accountability” through conversation in high and low income contexts. 38

Some initiatives from civil society can offer inspiration for public health practice and potential partners for building longer term trustworthiness among the institutions that need it in vaccine deployment. Many initiatives have focused on digital literacy, which arguably does not get to the root of the problem of mistrust, but others have tried to tackle it directly. Citizen journalist initiatives such as Chicas Poderosas in Argentina or Animal Político in Mexico, for example, have found effective new ways for citizens to hold political leaders accountable and for reconnecting citizens to their institutions. 39

A strong independent media, including active protection of journalists, is also key to challenging polarised political narratives and dissecting political decisions that affect community life. The new International Fund for Public Interest Media, for example, focuses on unlocking resources to empower independent media in low and middle income countries. 40 Providing practical, financial, and political support to media such as community radio and local online journalism in South Africa and Colombia helped to strengthen trust in information and devise creative solutions to collective problems. Political analysis of vaccines points us to the importance of these broader areas of work, the success of which is wrapped up in (re)building trust.

Vaccines are unavoidably political. From becoming symbols in broader struggles for inclusion to being co-opted in populist campaigns, an understanding of the political dimensions of vaccine confidence can help us to respond more effectively to the levers of mistrust. Rather than calling for depoliticisation, integrating political analysis into our programming can shed light on how broader contextual factors shape how people engage with (mis)information that they encounter online. This can support the development of deeper community engagement efforts that directly tackle these concerns and the identification of novel solutions to build trust in institutions and health systems.

Key messages

Understanding how politics shapes health outcomes can help to identify new ways to tackle global health challenges such as vaccine hesitancy

Focusing on individual exposure to misinformation may miss how collective experiences, as well as broader societal, historical, and political contexts, shape interpretation of information and decision making about immunisation

Political analysis can help public health workers, civil society, and researchers to devise novel solutions to confront the political drivers of vaccine hesitancy

Acknowledgments

LE acknowledges the support of a UK Research and Innovation future leaders fellowship (ref: MR/T040521/1).

Contributors and sources: LE has worked on the Ebola vaccine trials in Sierra Leone, as well as subsequent projects on vaccine confidence and delivery in the country. HD is working on infodemics and vaccine confidence. NB is a social scientist with the Health Security Agency-LSHTM Rapid Support Team and recently worked in cholera epidemics in Zambia and Malawi. MSS has experience in researching outbreaks and vaccinations in Uganda and the UK. MM is a political theorist researching community engagement in the context of epidemics. AM has worked on the Ebola vaccine trials in Sierra Leone as well as several studies on vaccine delivery in the country. AC is a research officer focused on health, who recently led a study on gendered dimensions of vaccination in Freetown. AN is a public health expert and leads the research working group at the National Public Health Agency in Sierra Leone. LE led on the conception, literature review, and writing of the article; HD and MM supported LE in drafting recommendations. All other authors were involved in editing and reviewing the article, as well as providing additional sources and insights. LE is the guarantor.

Competing interests: We have read and understood The BMJ policy on declaration of interests and have no interests to declare.

Provenance and peer review: Commissioned; externally peer reviewed.

This article is part of a collection that was proposed by the Advancing Health Online Initiative (AHO), a consortium of partners including Meta and MSD, and several non-profit collaborators ( https://www.bmj.com/social-media-influencing-vaccination ). Research articles were submitted following invitations by The BMJ and associated BMJ journals, after consideration by an internal BMJ committee. Non-research articles were independently commissioned by The BMJ with advice from Sander van der Linden, Alison Buttenheim, Briony Swire-Thompson, and Charles Shey Wiysonge. Peer review, editing, and decisions to publish articles were carried out by the respective BMJ journals. Emma Veitch was the editor for this collection.

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IRS enters next stage of Employee Retention Credit work; review indicates vast majority show risk of being improper

Highest-risk claims being denied, additional processing to begin on low-risk claims; heightened scrutiny and review continues as compliance work tops $2 billion; IRS will consult with Congress on potential legislative action before making decision on future of moratorium

IR-2024-169, June 20, 2024

WASHINGTON — Following a detailed review to protect taxpayers and small businesses, the Internal Revenue Service today announced plans to deny tens of thousands of improper high-risk Employee Retention Credit claims while starting a new round of processing lower-risk claims to help eligible taxpayers.

“The completion of this review provided the IRS with new insight into risky Employee Retention Credit activity and confirmed widespread concerns about a large number of improper claims,” said IRS Commissioner Danny Werfel. “We will now use this information to deny billions of dollars in clearly improper claims and begin additional work to issue payments to help taxpayers without any red flags on their claims.”

“This is one of the most complex credits the IRS has administered, and we continue to ask taxpayers for patience as we unravel this complex process,” Werfel added. “Ultimately, this period will help us protect taxpayers against improper payouts that flooded the system and get checks to those truly eligible.”

The review involved months of digitizing information and analyzing data since last September to assess a group of more than 1 million Employee Retention Credit (ERC) claims representing more than $86 billion filed amid aggressive marketing last year.

During this process, the IRS identified between 10% and 20% of claims fall into what the agency has determined to be the highest-risk group, which show clear signs of being erroneous claims for the pandemic-era credit. Tens of thousands of these will be denied in the weeks ahead. This high-risk group includes filings with warning signals that clearly fall outside the guidelines established by Congress.

In addition to this highest risk group, the IRS analysis also estimates between 60% and 70% of the claims show an unacceptable level of risk. For this category of claims with risk indicators, the IRS will be conducting additional analysis to gather more information with a goal of improving the agency’s compliance review, speeding resolution of valid claims while protecting against improper payments.

At the same time, the IRS continues to be concerned about small businesses waiting on legitimate claims, and the agency is taking more action to help. Between 10% and 20% of the ERC claims show a low risk. For those with no eligibility warning signs that were received prior to the last fall’s moratorium, the IRS will begin judiciously processing more of these claims.

The IRS anticipates some of the first payments in this group will go out later this summer. But the IRS emphasized these will go out at a dramatically slower pace than payments that went out during the pandemic period given the need for increased scrutiny.

As the additional IRS processing work begins at a measured pace, other claims will begin being paid later this summer following a final review. This additional review is needed because the submissions may have calculation errors made during the complex filings. For those claims with calculation errors, the amount claimed will be adjusted before payment.

The IRS also noted that generally the oldest claims will be worked first, and no claims submitted during the moratorium period will be processed at this time.

No additional action needed by taxpayers at this time; await further notification from the IRS

The IRS cautioned taxpayers who filed ERC claims that the process will take time, and the agency warned that processing speeds will not return to levels that occurred last summer. Taxpayers with claims do not need to take any action at this point, and they should await further notification from the IRS. The agency emphasized those with ERC claims should not call IRS toll-free lines because additional information is generally not available on these claims as processing work continues.

“These complex claims take time, and the IRS remains deeply concerned about how many taxpayers have been misled and deluded by promoters into thinking they’re eligible for a big payday. The reality is many aren’t,” Werfel said. “People may think they are on safe ground, but many are simply not eligible under the law. The IRS continues to urge those with pending claims to use this period to review the guideline checklist on IRS.gov, talk to a legitimate tax professional rather than a promoter and use the special IRS withdrawal program when there’s an issue.”

Werfel also cautioned taxpayers to be wary of promoters using today’s announcement as a springboard to attract more clients to file ERC claims.

“The whole world has changed involving Employee Retention Credits since the deepest days of the pandemic,” Werfel said. “Anyone applying for this credit needs to talk to a trusted tax professional and closely review the eligibility requirements, not someone playing fast and loose and trying to make a fast buck off well-meaning taxpayers. People need to be cautious of promoters trying to take advantage of today’s announcement to drive more business. People should remember the IRS continues to be very active in our compliance lanes on Employee Retention Credits.”

Steps taken since September 2023 when processing moratorium on new ERC claims began

During the ERC review period, the IRS continued to process claims received prior to September 2023. The agency processed 28,000 claims worth $2.2 billion and disallowed more than 14,000 claims worth more than $1 billion.

The ERC program began as a critical effort to help businesses during the pandemic, but the program later became the target of aggressive marketing well after the pandemic ended. Some promoter groups may have called the credit by another name, such as a grant, business stimulus payment, government relief or other names besides ERC or the Employee Retention Tax Credit (ERTC).

To counter the flood of claims being driven by promoters, the IRS announced last fall a moratorium on processing claims submitted after Sept. 14, 2023, to give the agency time to digitize information on the large study group of nearly 1 million ERC claims, which are made on amended paper tax returns. The subsequent analysis of the results during this period helped the IRS evaluate next steps, providing the IRS valuable information to change the way the agency will process ERC claims going forward.

The findings of the IRS review confirmed concerns raised by tax professionals and others that there was an extremely high rate of improper ERC claims.

The claims followed a flurry of aggressive marketing and promotions last year that led to people being misled into filing for the ERC. After the moratorium was put in place on Sept. 14, the IRS has continued to see ERC claims continuing to come in at the rate of more than 17,000 a week, with the ERC inventory currently at 1.4 million.

In light of the large inventory and the results of the ERC review, the IRS will keep the processing moratorium in place on ERC claims submitted after Sept. 14, 2023. The IRS will use this period to gather additional feedback from partners, including Congress and others, on the future course of ERC.

“We decided to keep the post-September moratorium in place because we continue to be concerned about the substantial number of claims coming in so long after the pandemic,” Werfel said. “These claims are clogging the system for legitimate taxpayers. We worry that ending the moratorium might trigger a gold rush by aggressive marketers that could lead to a new round of improper claims, which would be a bad result for taxpayers or tax administration. We will use this time to consult with Congress and seek additional help from them on the ERC program, including potentially closing down new claims entirely and seeking an extension of the statute of limitations to allow the agency more time to pursue improper claims.”

Special IRS Withdrawal Program remains open for those with unprocessed ERC claims

Given the large number of questionable claims indicated by the new review, the IRS continues to urge those with unprocessed claims to consider the special IRS ERC Withdrawal Program to avoid future compliance issues.

Businesses should quickly pursue the claim withdrawal process if they need to ask the IRS to not process an ERC claim for any tax period that hasn’t been paid yet. Taxpayers who received an ERC check — but haven’t cashed or deposited it — can also use this process to withdraw the claim and return the check. The IRS will treat the claim as though the taxpayer never filed it. No interest or penalties will apply.

With more than 1.4 million unprocessed ERC claims, the claim withdrawal process remains an important option for businesses who may have submitted an improper claim.

IRS compliance work tops $2 billion from Voluntary Disclosure Program, withdrawal process, disallowances

The IRS also announced today that compliance efforts around erroneous ERC claims have now topped more than $2 billion since last fall. This is nearly double the amount announced in March following completion of the special ERC Voluntary Disclosure Program (VDP), which the IRS announced led to the disclosure of $1.09 billion from over 2,600 applications. The IRS is currently considering reopening the VDP at a reduced rate for those with previously processed claims to avoid future compliance action by the IRS.

Compliance work on previously processed ERC claims continue, and work continues on a number of efforts to counter questionable claims:

The ongoing claim withdrawal process for those with unprocessed ERC claims has led to more than 4,800 entities withdrawing $531 million.
The IRS has determined that more than 12,000 entities filed over 22,000 claims that were improper and resulted in $572 million in assessments. This initial round of letters covers Tax Year 2020. Thousands more of these letters are planned in coming months to address Tax Year 2021, which involved larger claims. Congress increased the maximum ERC from $5,000 per employee per year in 2020, to $7,000 per employee for each quarter of the year in 2021.
More than 2,600 applications for the special ERC Voluntary Disclosure Program (VDP) , which ended in March, disclosed $1.09 billion.

The IRS is currently assessing whether to reopen the special ERC Voluntary Disclosure Program to help taxpayers get into compliance on paid claims and avoid future IRS compliance action, including audits. If the program reopens, the IRS anticipates the terms will not be as favorable as the initial offering that closed in the spring. A decision will be made in coming weeks.

The IRS also reminded those with pending claims or considering submitting an ERC claim about other compliance actions underway:

Criminal investigations: As of May 31, 2024, IRS Criminal Investigation has initiated 450 criminal cases, with potentially fraudulent claims worth nearly $7 billion. In all, 36 investigations have resulted in federal charges so far, with 16 investigations resulting in convictions and seven sentencings with an average sentence of 25 months.

Audits: The IRS has thousands of ERC claims currently under audit.

Promoter investigations: The IRS is gathering information about suspected abusive tax promoters and preparers improperly promoting the ability to claim the ERC. The IRS’s Office of Promoter Investigations has received hundreds of referrals from internal and external sources. The IRS will continue civil and criminal enforcement efforts of these unscrupulous promoters and preparers.

Help for businesses with eligibility questions and those misled by promoters

Some promoters told taxpayers every employer qualifies for ERC. The IRS and the tax professional community emphasize that this is not true. Eligibility depends on specific facts and circumstances. The IRS has dozens of resources to help people learn about and check ERC eligibility and businesses can also consult their trusted tax professional . Key IRS materials to help show taxpayers if they have a risky ERC claim include:

ERC Eligibility Checklist (interactive version and a printable guide PDF ) includes cautions about common areas of misinformation and links to facts and examples.
7 warning signs ERC claims may be incorrect outlines tactics that unscrupulous promoters have used and why their points are wrong.
Frequently asked questions about the Employee Retention Credit includes eligibility rules, definitions, examples and more.
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IRS enters next stage of Employee Retention Credit work; review indicates vast majority show risk of being improper

Highest-risk claims being denied, additional processing to begin on low-risk claims; heightened scrutiny and review continues as compliance work tops $2 billion; IRS will consult with Congress on potential legislative action before making decision on future of moratorium

No additional action needed by taxpayers at this time; await further notification from the IRS

Steps taken since September 2023 when processing moratorium on new ERC claims began

Special IRS Withdrawal Program remains open for those with unprocessed ERC claims

IRS compliance work tops $2 billion from Voluntary Disclosure Program, withdrawal process, disallowances