systems thinking in research

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The application of systems thinking in health: why use systems thinking?

David h peters.

Department of International Health, Johns Hopkins University Bloomberg School of Public Health, Room E8527, 615 N Wolfe St, Baltimore, MD 21205 USA

This paper explores the question of what systems thinking adds to the field of global health. Observing that elements of systems thinking are already common in public health research, the article discusses which of the large body of theories, methods, and tools associated with systems thinking are more useful. The paper reviews the origins of systems thinking, describing a range of the theories, methods, and tools. A common thread is the idea that the behavior of systems is governed by common principles that can be discovered and expressed. They each address problems of complexity, which is a frequent challenge in global health. The different methods and tools are suited to different types of inquiry and involve both qualitative and quantitative techniques. The paper concludes by emphasizing that explicit models used in systems thinking provide new opportunities to understand and continuously test and revise our understanding of the nature of things, including how to intervene to improve people’s health.

In the rapidly changing field of global health, it is hard to know whether the recent attention to systems thinking is just another fad, or something more durable that offers usable insights for understanding and action. Some see systems thinking as providing a powerful language to communicate and investigate complex issues, while others are confused by the sizable and amorphous body of theories, methods, and tools involved. Time will tell, of course, but in the meantime, it is helpful to consider why we would use systems thinking in a field that already draws upon a rich collection of theories, methods, and tools from the health sciences, social sciences, engineering, mathematics, and other disciplines.

From mental models to explicit ones

At its core, systems thinking is an enterprise aimed at seeing how things are connected to each other within some notion of a whole entity. We often make connections when conducting and interpreting research, or in our professional practice when we make an intervention with an expectation of a result. Anytime we talk about how some event will turn out, whether the event is an epidemic, a war, or other social, biological, or physical process, we are invoking some mental model about how things fit together. However, rather than relying on implicit models, with hidden assumptions and no clear link to data, systems thinking deploys explicit models, with assumptions laid out that can be calibrated to data and repeated by others. The word system is derived from the Greek sunistánai , meaning “to cause to stand together.” If we consider that a system is a perceived whole, made up of parts that interact toward a common purpose, we recognize that the ability to perceive, and the quality of that perception, is also part of what causes a system to stand together. Systems thinking is intended to improve the quality of those perceptions of the whole, its parts, and the interactions within and between levels.

Every interpretation of a research result involves a model, whether it is a physical model used for experimentation, a statistical model used to estimate the relationships between variables, or a conceptual model about how elements are connected. A model is simply a way we compactly represent and understand an object, phenomenon, or system. As much as research involves observation and experimentation, I would argue that good research is also about building and using explicit models rather than implicit ones. The real question is not whether we should be using systems thinking, as broadly described here, but rather, which of the many theories, methods, and tools currently associated with the field of systems thinking are most useful in particular settings.

For example, where individual people interact directly with one another (e.g., transmitting disease) while moving about in an explicit space such as a city, agent-based modeling [ 1 , 2 ] may be especially powerful. In modeling how different agencies within a large public health system interact, social network theory [ 3 ] could be more directly relevant.

Systems thinking has largely developed as a field of inquiry and practice in the 20 th century, and has multiple origins in disciplines as varied as biology, anthropology, physics, psychology, mathematics, management, and computer science. The term is associated with a wide variety of scientists, including the biologist Ludwig von Bertalanffy who developed General System Theory; psychiatrist Ross Ashby and anthropologist Gregory Bateson who pioneered the field of cybernetics; Jay Forrester, a computer engineer who launched the field of systems dynamics; scientists at the Santa Fe Institute, such as Noble Laureates Murray Gell-Mann and Kenneth Arrow, who have helped define complex adaptive systems [ 4 ]; and a wide variety of management thinkers, including Russell Ackoff, a pioneer in operations research, and Peter Senge, who has popularized the learning organization. Much of the work in systems thinking has involved bringing together scientists from many disciplinary traditions, in many cases allowing them to transfer methods from one discipline to another (inter-disciplinarity), or to work across and between disciplinary boundaries, creating learning through a wide variety of stakeholders, including researchers and those affected by the research (trans-disciplinarity).

Theories, methods, and tools

If there is a jungle of terminology used to describe scientific endeavor, it gets even thicker in the area of systems thinking, perhaps because of its diverse heritage. Given the varied disciplines and trans-disciplinary traditions involved, it is easy to see why people often talk about broader “approaches”, “perspectives”, or “lenses” when applying systems thinking. Systems thinking models and frameworks are sometimes grand and widely applicable, such as General System Theory, and at other times very specifically applied to particular phenomena, such as the theory on critical points in physics, which is used to explain the point at which a material behaves as neither liquid or gas (or solid). Systems thinking can involve a wide range of theories, which are rational sets of ideas or principles intended to explain something. It is based on a wide variety of scientific methods used to investigate phenomena and acquire knowledge. It uses an even larger array of instruments or tools – the hardware and software used to conduct experiments, make observations, or collect and analyze data. The use of these terms is not consistent across or within scientific fields, including systems sciences, and the continuum from tool to method to theory and framework is often blurry.

Rather than attempt to sort out semantic nuances between these terms, the utility of systems thinking can be better appreciated by a brief look at some of its more commonly used theories, methods, and tools (Table  1 ). The theories and methods in systems thinking are each designed to address complex problems. They are complex because they involve multiple interacting agents, the context in which they operate keeps changing, because the manner in which things change do not conform to linear or simple patterns, or because elements within the system are able to learn new things, sometimes creating new patterns as they interact over time. Many of the challenges in global health are now recognized as complex problems where simple blueprint approaches have limited success [ 5 , 6 ].

Systems thinking theories, methods, and tools

Systems thinking tools have a wide variety of applications. Some tools are intended as means of facilitating groups of people to have a common understanding about an issue to prompt further inquiry and action. For example, “systems archetypes” help teams to understand generic patterns of interaction that can be applicable to their “story” [ 24 ]. Rather than use the pre-existing templates of systems archetypes, causal loop diagrams (CLD) are created without a template, and involve drawing out people’s understanding of how elements of a problem are related to each other [ 19 ]. They usually begin as qualitative descriptions outlining how one thing causes another in either a positive or negative direction. Typically, feedback loops are identified between the different elements. They can be reinforcing or positive feedback loops, where A produces more B which in turn produces more A, such as the vicious cycle of under-nutrition and infection. They can also be balancing or negative feedback loops, where a positive change in one leads to a push back in the opposite direction, such as when increasing body temperature produces sweating, which in turn cools down the body. In this supplement, a number of studies use CLDs that describe relationships between different elements of a health system to explain phenomena such as dual practice of health workers in Uganda [ 25 ], provider payment systems in Ghana [ 26 ], and childhood vaccination coverage in India [ 27 ].

The elements of a CLD might also be converted into a quantitative systems dynamics model by classifying the elements as “stocks”, “flows”, or “auxiliary” variables, and using equations to describe the relationships between individual variables in one of many available systems dynamics software environments. In this supplement, Rwashana and colleagues use systems dynamics models to examine neonatal mortality in Uganda [ 28 ], while other authors use systems dynamics models to examine the effects of policy interventions [ 29 ].

There are number of other tools that are used to map out events or how things are connected. Network mapping, social network analyses, and process mapping involve a range of tools to illustrate and analyze connections between people, organizations, or processes in both qualitative and quantitative ways. In this supplement, Malik et al. map out the network of actors involved in physician’s seeking advice in Pakistan [ 30 ]. The flow chart is one of the more common tools used to draw a process or a system. Innovation history (or change management history) is used to compile a history of key events, outcomes, issues that have cropped up along the way, and measures taken to address problems. In this supplement, Zhang et al. [ 31 ] look back over the last 35 years of the development of the medical system in rural China. Participatory Impact Pathways Analysis involves workshops and a combination of tools to clarify the logic of interventions and a mapping of the network [ 21 ]. It is intended to enhance understanding through participation with beneficiaries, implementers, and other stakeholders in a project. Several papers in this supplement use similar approaches for a variety of situations, including to build leadership capacity for health systems in South Africa [ 32 ], to develop sustainable physical rehabilitation programs in Nepal and Somaliland [ 33 ], and to build sustainable maternal and child preventive health services in Northern Bangladesh [ 34 ].

Agent based modeling takes advantage of a wide variety of theories, methods, and tools to build computer models that simulate the interaction of agents (e.g., individuals or organizations) to see how real world phenomena “grow” and affect the system as a whole. The models involve multiple individual agents that work at different scales, some decision-making rules (e.g., simple rules on how they reproduce, interact with others or pursue objectives), processes for adaptation, and a space in which the agents operate.

In global health, we are concerned with both theory and practice, and are in need of models that match the complex conditions in which we work. A common thread of all these theories, methods, and tools is the idea that the behavior of systems is governed by common principles that can be discovered and expressed. They are all helpful in trying to conceptualize the systems in place. Some are more focused on ways to change the system to produce better outcomes. In using these theories, methods, and tools, we are reminded by the statistician George EP Box that “ all models are wrong, but some are useful ” [ 35 ]. It is to these uses that we now turn.

In much of public health and medicine, we use research evidence on the efficacy of interventions to inform decisions with an expectation about their future effect. Some systems thinking methods and tools, such as scenario planning, can also be used to explicitly forecast future events. However, even then, such methods are intended to be used for identifying possible outcomes to provide insights on how to prepare for them rather than fixing on any particular outcome.

In his landmark address on “Why Model?”, which provided inspiration for this essay, Joshua Epstein identified 16 reasons other than prediction on why to model [ 36 ]. Most of these reasons are applicable to systems thinking more broadly. Many of these specific reasons relate to being able to explain how things work, and systems thinking is particularly useful to explaining how complex systems work. Many of models can be used for testing the viability of policy interventions in a safe and inexpensive way – agent based models, systems dynamics models, and scenario planning are particularly useful for these purposes. In this journal supplement, for example, Bishai et al. present a very simple systems dynamics model to illustrate the trade-offs and unintended consequences of policy choices related to allocation to preventive and curative services [ 29 ].

Systems thinking approaches can also provide guidance on where to collect more data, or to raise new questions and hypotheses. The methods and tools help us to make explicit our assumptions, identify and test hypotheses, and calibrate our models against real data. One of the frustrations of health planners and researchers has been the aspiration that interventions shown to be effective at small scale or in a research setting cannot be simply replicated at large scale or to reach populations that are most vulnerable. Systems thinking methods and tools are increasingly being used to explain epidemics and to inform programmatic expansion efforts [ 5 , 6 ].

One of the more compelling reasons to use systems thinking approaches is to inspire a scientific habit of mind. Beyond the contributions of any particular theory, method, or tool, the practice of systems thinking can reinforce what Epstein calls a “militant ignorance”, or commitment to the principle that “I don’t know” as a basis for expanding scientific knowledge. Systems thinking adds to the theories methods and tools we otherwise use in global health, and provides new opportunities to understand and continuously test and revise our understanding of the nature of things, including how to intervene to improve people’s health. And for those who value thinking and doing in global health, that can only be a good thing.

Acknowledgements

This Commentary is part of the Thematic Series entitled: “Advancing the application of systems thinking in health”. The Series was coordinated by the Alliance for Health Policy and Systems research, World Health Organization with the aid of a grant from the International Development Research Centre, Ottawa, Canada. The author also gratefully acknowledges support from the Future Health Systems Research Programme Consortium through a grant provided from the Department for International Development (United Kingdom). I also appreciate the comments received from Josh Epstein.

Competing interests

The author declares that he has no competing interests.

What 'systems thinking' actually means - and why it matters for innovation today

Systems thinking helps us see the part of the iceberg that's beneath the water Image:  Ezra Jeffrey

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Stay up to date:.

• Systems thinking can help us grasp the interconnectedness of our world.
• During the uncertainty of the pandemic, it can spur innovation.

We are currently living through VUCA (volatile, uncertain, complex and ambiguous) times.

As innovators, general professionals, key workers, citizens and humans, everything we do is ever more interdependent on each other. ‘No man is an island’ is a well-known phrase, yet in practice, how often do we understand the interconnectedness of everything around us? Enter systems thinking.

In some circles, there has been a lot of hype around taking an "ecosystems view" during this global pandemic, which frankly is not something new. Systems thinking has been an academic school of thought used in engineering, policy-making and more recently adapted by businesses to ensure their products and services are considering the ‘systems’ that they operate within.

Defining innovation

Every firm defines innovation in a different way. I enjoy using the four-quadrant model (see figure below) for simplicity: incremental innovation utilises your existing technology within your current market; architectural innovation is applying your technology in different markets; disruptive innovation involves applying new technology to current markets; and radical innovation displaces an entire business model.

During COVID-19, we are seeing a mixture of these. Many firms will start with incremental changes, adapting their products to a new period of uncertainty. With the right methodology and balance of internal and external capabilities, there is potential for radical and disruptive innovation that meets new needs, or fundamentally, creates new needs based on our current circumstances. Systems thinking is essential in untapping these types of innovation and ensuring they flourish long-term.

A dynamic duo

‘Systems thinking’ does not have one set toolkit but can vary across different disciplines, for example, in service design some may consider a ‘blueprint’ a high-level way to investigate one’s ‘systems of interest’. Crucially, this school of thought is even more powerful when combined with more common approaches, such as human-centered design (HCD).

The latter is bottom-up – looking in detail at a specific problem statement, empathising with its users and developing solutions to target them. Whereas the former is top-down – understanding the bigger picture, from policy and economics to partnerships and revenue streams. Systems thinking unpacks the value chain within an organisation and externally. It complements design thinking: together they’re a dynamic duo.

For starters, this philosophy needs to enter our everyday thinking. Yes, it is crucial for innovation, but an easy first step is to use systems thinking casually throughout your life. How is this purchase affecting other systems in the supply chain? What is the local economic impact of me shopping at the larger supermarket? Who will be the most negatively impacted if I don’t practice social distancing?

This mapping tool from the World Economic Forum is central in understanding causal relationships and effects during COVID-19. It helps to drive systems-informed decision making. Once this becomes mainstream, we can begin integrating data for systems modelling tools that will help us map impact across the multiple layers of influence from this pandemic. So, what does this mean for businesses?

Systems thinking for business

To illustrate how systems thinking applies in business, let's use a simplified example of a bank branch.

Event: COVID-19 declared a pandemic, lockdown implemented for all people and businesses, except key workers and essential firms. Branches are shutting, people are afraid to go to non-essential establishments.

Patterns/trends: what trends have there been over time? Scientists have warned us about being ‘pandemic-ready’ for years, but we have had misinformation or a lack of transparency from other ‘systems’ who should have been driving this.

However, what about banking patterns? More customer service has moved online, digital banks and fintech developments have decreased the urgency for face-to-face business in branches. Are there trends in customer behaviours? More consumers are searching for all their products and services online, and this was common before the pandemic had begun.

Underlying structures: what has influenced these patterns and how are they interconnected? A growing desire for digitalised experiences and convenience is popular in financial services and customers will begin to seek and only interact with businesses who have the infrastructure to operate this way. A minimal number of touchpoints is seen as desirable, providing quicker, stress-free experiences, as consumers want to spend less time on these engagements when work-life balance has become more integrated, and therefore is important to preserve.

Mental models: what assumptions, beliefs and values do people hold about the system? Behavioural economics tells us that customers will adapt and change their consumer spending habits. Used to the convenience of online, less relevance will be seen for branches, and banks will need to further adapt. The ‘new normal’ will contain old and new beliefs. Which ones keep bank branches in place? Human contact and customer service? The agency in dealing with your finances face-to-face? Will a new experience or service be required to keep bank branches relevant or are online digital banks all consumers will need?

Beyond this, do banks have an ethical obligation to monitor spending habits to identify signs of debt and underlying mental health problems? What relationship should banks have with data? How do they balance intuitive service with consumer privacy?

Going through the layers of this iceberg unearths part of the power from using systems thinking and exemplifies how to guide your strategy in a sustainable way.

Only focusing on events? You’re reacting.

Thinking about patterns/trends? You’re anticipating.

Unpicking underlying structures? You’re designing.

Understanding mental models? You’re transforming.

Transformative thinking is how we innovate and systems thinking is essential for this journey.

We’ve only explored the tip of the iceberg (pun intended) on the philosophy of systems thinking. There are many in-depth tools available to discover the approach in more depth.

Ask yourselves if you want to survive the VUCA future ahead. Do you want your organisation to have the capacity to innovate and sustain itself? Are you willing to change your thought pattern to consider the systems in which we all live in?

If the answers to any of the questions above are yes, then you are on the right path to mastering systems thinking to successfully innovate.

The more we begin to use systems thinking every day, the better our innovation will become. We can all be architects for a better world with sustainable growth if we understand the core tenants of this approach. To echo my introduction, no customer, or citizen, or business, or policy, or company, or idea itself is an island. Whatever ‘new normal’ we have, systems thinking should drive this future and will ensure innovation is pursued with knowledge of the complex intricacies that we are living through.

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World Economic Forum articles may be republished in accordance with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License, and in accordance with our Terms of Use.

The views expressed in this article are those of the author alone and not the World Economic Forum.

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Ask an MIT Professor: What Is System Thinking and Why Is It Important?

Prof. edward crawley considers system thinking “ the cognitive skill of the 21st century”.

By: MIT xPRO

“System Thinking is the cognitive skill of the 21st century.”

Look around you, and you’ll see: life as we know it is becoming more and more complex.

From the iPhone in your pocket to the organizations driving public health, national defense, finance, criminal justice, and [insert just about anything else you can imagine], the world is powered by increasingly intricate systems working behind the scenes to integrate countless moving pieces into a meaningful whole.

“One of the characteristics of the 21st century is that we’re investing more in complexity, and things are just getting damn complicated,” says Professor Edward Crawley , Ford Department of Engineering, Department of Aeronautics and Astronautics, MIT.

Crawley is one of the MIT lead faculty instructors for MIT xPRO’s online course on system thinking  , a skill that helps organizations examine and simplify complexity, recognize patterns, and create effective solutions to challenges. He considers system thinking “ the cognitive skill of the 21st century.” We recently sat down with him to discuss system thinking and what learners can expect from his course.

What is system thinking?

Prof. Crawley explains that “system thinking is simply thinking about something as a system: the existence of entities-the parts, the chunks, the pieces-and the relationships between them.”

There are measures of both performance and complexity in system thinking. “Complexity is what we invest in: more parts, more sophisticated parts, more parts talking to more parts,” Crawley states. “Performance is the benefit that emerges.”

Who uses system thinking, and how might they use it?

“System thinking is for everyone on this side of the life-death line,” Prof. Crawley jokes. Anyone who has taken a course he teaches will tell you that he has an excellent sense of humor.

More specifically, system thinking is broadly used by:

• Leaders who have a high-level view of how different parts of a system fit together and need to be able to step back and see how all the pieces connect.
• Individual contributors who want to understand how the part they’re responsible for fits into the bigger picture so that they can perform at their highest potential.

While it’s true that system thinking is prevalent in STEM fields, Crawley stresses that a tremendous amount of system thinking occurs outside of science, technology, engineering, and mathematics. He rattles off a list of examples to illustrate his point: “The legal system is a system , the Constitution is a system , public health is a system , national defense is a system , finance is a  system .”

In a professional setting, leaders and individual contributors use system thinking all the time to understand:

• How organizations work ( e.g. , team dynamics)
• Complex technologies ( e.g. , smartphones and other devices)
• The optimal ways to track, organize, and utilize information ( e.g. , medical records)
• Intricate processes ( e.g. , the tax system: who pays taxes, how much they pay, and how the revenue is distributed)

Crawley specializes in using system thinking to understand the space system, exploring the answers to questions like: Who builds the satellites? What orbits are they in? How do they communicate with each other? How can humans produce brilliant images like those from the James Webb Space Telescope  ? “Those images are an example of an emergent value proposition that resulted from NASA’s multi-year effort on the James Webb Space Telescope,” remarks Crawley.

What pedagogical methods and tools do you use to get learners comfortable with system thinking?

The big challenge in being one of the faculty instructors for MIT xPRO’s system thinking course, explains Prof. Crawley, is using examples that exhibit just the right amount of complexity. The systems need to be complicated enough that the answers aren’t too obvious but not so complicated that no one can understand how they work, even after learning the tools for system thinking.

Crawley prefers using examples that he categorizes as “middle-complexity systems that people commonly encounter in their lives.” One example is a bicycle. If a rollerblade is too simple and an automobile is overly complex, a bicycle is just right. “You want to train your mind and train your methodology to think about automobiles, but it’s a hard place to start,” says Crawley. “So you start with the middle-complexity system.”

Crawley uses these types of examples to teach students:

• The principles underlying the system
• The methods used to think about the system
• The concrete tools that system thinkers activate each day

What are some challenges learners face during a system thinking course?

Nevertheless, getting comfortable with system thinking can be extraordinarily challenging for learners! Why? Because system thinking is, in essence, an entirely new way of thinking.

“You’re literally neurologically tuning up your brain. You’re creating connections between neurons that didn’t exist before because you’re developing new neural pathways that allow you to think about things differently,” states Crawley.

“I tell my class at MIT at the beginning of the term, ‘I predict that within a week or two, you’ll have headaches,’” he says with a grin. “They look at me and laugh. But sure enough, I check in with them two weeks later, and I’m right.”

What would you say to someone considering enrolling in a system thinking course?

“You’ll get over the headaches once the brain is rewired,” Prof. Crawley jokes.

On a serious note, Crawley encourages students to take a system thinking course because learning a new way of thinking about the world is of vital importance in the 21st century.

“Life is only getting more complex,” he says. If you see him in person, ask him to tell the story about how he and a colleague — two actual rocket scientists — couldn’t figure out how to make a photocopy. “That was two decades ago, and already technology was so complex that you had to be trained to operate it!” he exclaims.

With devices and organizations becoming ever more complicated, system thinking can give learners the skills to succeed.

Those skills include being able to engage in the unknown and think differently about the relationships between the parts that make up a system; ultimately, learners evolve from reductionist thinkers to integrative thinkers ready to face a limitless future.

If you’d like the opportunity to learn from Professor Crawley, as well as Professors John Sterman, Daniela Rus, and Hasma Balakrishnan, enroll in MIT xPRO’s 5-week online system thinking course  .

Originally published at http://curve.mit.edu on September 14th, 2022.

Ask an MIT Professor: What Is System Thinking and Why Is It Important? was originally published in MIT Open Learning on Medium, where people are continuing the conversation by highlighting and responding to this story.

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A review of implementation and evaluation frameworks for public health interventions to inform co-creation: a Health CASCADE study

• Giuliana Raffaella Longworth   ORCID: orcid.org/0000-0001-9063-4821 1 ,
• Kunshan Goh 3 ,
• Danielle Marie Agnello 4 ,
• Katrina Messiha 3 ,
• Melanie Beeckman 6 ,
• Jorge Raul Zapata-Restrepo 2 ,
• Greet Cardon 5 ,
• Sebastien Chastin 4 , 5 &
• Maria Giné-Garriga 2  

Health Research Policy and Systems volume  22 , Article number:  39 ( 2024 ) Cite this article

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By including the needs and perspectives of relevant stakeholders, co-creation is seen as a promising approach for tackling complex public health problems. However, recommendations and guidance on how to plan and implement co-creation are lacking. By identifying and analysing existing implementation and evaluation frameworks for public health, this study aims to offer key recommendations for professional stakeholders and researchers wanting to adopt a co-creation approach to public health interventions.

Firstly, PubMed and CINAHL databases were screened for articles introducing original implementation and evaluation frameworks for public health interventions. Backwards snowballing techniques were applied to the included papers. Secondly, identified frameworks were classified and relevant data extracted, including steps and constructs present in the frameworks. Lastly, recommendations were derived by conducting thematic analysis on the included frameworks.

Thirty frameworks were identified and data related to their nature and scope extracted. The frameworks’ prominent steps and constructs were also retrieved. Recommendations related to implementation and evaluation in the context of co-creation were included.

When engaging in co-creation, we recommend including implementation considerations from an early stage and suggest adopting a systems thinking as a way to explore multiple levels of influence, contextual settings and systems from an early planning stage. We highlight the importance of partnering with stakeholders and suggest applying an evaluation design that is iterative and cyclical, which pays particular attention to the experience of the engaged co-creators.

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Implementation science has been defined as the transfer of clinical research findings and evidence-based results into the real world and hence how a study can affect or hinder its uptake in the routine practice [ 1 , 2 , 3 , 4 ]. Thus, implementation science is set to observe and study the gap between, on one side, a solution developed in a controlled environment and, on the other, the specific context where the intervention is applied by looking at contextual factors that may act as barriers or facilitators.

However, interventions and solutions built in a controlled setting and transferred to specific context, have been argued to obtain limited success, mostly in the long term. For instance, the misconsideration for complex systems and factors related to settings and the targeted population have been said to influence the lack of effectiveness [ 5 , 6 ]

Taking into account the relevance and inclusion of stakeholders’ knowledge in research production as been put forward as a possible way to address the research-practice gap [ 2 , 3 ]. For this reason, more recently, implementation science has been experiencing a shifts from this type of linear and controlled production models to more iterative participatory and complex models [ 7 , 8 , 9 ] with the design and creation of solutions and interventions directly in the real world.

Involving relevant stakeholders from the earliest stage of intervention design and/or implementation has been considered a way to increase uptake and positively affect not only patient satisfaction but also the quality of the service [ 10 , 11 , 12 , 13 , 14 , 15 ]. In line with this considerations, co-creation has been brought to the forefront as an opportunity to increase the successful uptake of evidence-based interventions and practices through meanginful and deep engagement of key stakeholders [ 16 , 17 , 18 , 19 ].

Co-creation is a collaborative approach of creative problem solving engaging diverse stakeholders at all project stages, from determining and defining the problem through to the final stages of a project [ 20 ]. By facilitating collaboration among key stakeholders, co-creation aims to taking into account social determinants and contextual factors that may influence the intervention’s feasibility and acceptability from the earliest stage of intervention design.

Considering co-creation’s intention to work within real-life settings and conditions, systems thinking seems to be a valuable approach to explore and potentially adopt when designing and evaluating co-creation. Adopting a system thinking approach would allow assessing contextual elements from an early stage of the intervention and gathering considerations around systemic factors that may influence the public health issue [ 21 ].

The need for formative evaluation in co-creation has been argued to be crucial to co-creation processes. An evaluation is intended to be formative when the implementation team and/or staff use data to improve or adapt the process of implementation [ 1 ]. Van Dijk-de Vries [ 22 ] argues that researchers, when co-creating, should assess the stakeholders’ engagement to ensure their perceptions are captured, suggesting this happens throughout the implementation. Formative evaluation would enable, if needed, to adapt and adjust the intervention.

Despite research advancement in the field, implementation guidance and recommendations for the planning and implementation of co-creation processes are lacking as existing implementation and evaluation frameworks have not been designed specifically for such approaches. The need to develop dedicated implementation and evaluation guidelines for co-creation lies in the distinctive nature of co-creation approaches, involving collaborative efforts with diverse stakeholders, emphasizing shared decision-making, innovation and creativity.

Implementation and evaluation guidance needs to be further developed to address the dynamic and participatory nature of these processes and the unique challenges of fostering meaningful partnerships, navigating diverse perspectives and harnessing collective creativity. Closing this gap is essential not only for the successful implementation of co-creation initiatives but also for unlocking the full potential of these collaborative efforts within the broader landscape of public health interventions. This study aims to address this gap by reviewing existing frameworks and offering an overview of recommendations that may guide the design and implementation of co-created interventions.

Eligibility criteria

To be included, studies had to describe or introduce an implementation or evaluation framework, therefore describing or introducing a framework representing key stages, factors, constructs or variables that explain or influence the implementation and/or evaluation of programs/interventions. Frameworks had to be generalizable, and therefore designed to be applicable for all public health topics. For the scope of this review, an intervention was defined as a set of actions with a coherent objective to bring about change or produce identifiable outcomes [ 26 ]. We identified the Promoting Action on Research Implementation in Health Services (PARIHS), designed in 1998, as our start date search, as one of the first frameworks to make explicit the multi-dimensional, complex nature of implementation and highlight the central importance of context [ 27 ]. Table 1 includes further details on applied criteria.

Search strategy

This scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for producing the PRISMA flow diagram (see Additional file 1 ). A specialist librarian was involved in developing the search strategy on PubMed and CINAHL. These were equal search strategies adapted for each database. The detailed search is available in Additional file 2 . In addition, we applied a snowballing technique of pursuing backwards references cited in selected publications.

Process of selection

At least two authors were independently involved in all review studies through each stage [ 23 ]. D.A., K.M., J.Z., M.B., M.G. and G.L. were involved in the title and abstract screening. All the latter co-authors, with the addition of K.G., conducted the full-text screening. D.A., K.M., J.Z., M.B., M.G., G.L. and K.G. took part in the data extraction, and K.G. and G.L. conducted the data analysis. Authors conducted individual and independent reviews through the software Rayyan. Footnote 1 Discrepancies were resolved by consensus and, if unresolved, by the involvement of a third reviewer (M.G., G.L., G.C.).

Included frameworks are reported in Fig.  1 . From each framework, we extracted prominent constructs (Additional file 3 ) and steps (Additional file 4 ). For extraction purposes, we defined constructs as a fundamental unit of thought, smaller than a judgment or theory but integral to them [ 24 ]. Identified steps were presented by categorizing them into the Leask’s et al. [ 25 ] framework included stages of planning, conducting, evaluating and reporting [ 25 ].

Classification of extracted frameworks

Data extraction

An Excel template was developed to extract information relate to the framework’s nature, type and scope, positioning of implementation and evaluation considerations within the frameworks, and type of evaluation covered and other elements (see Table  2 for full list of data extracted). For each framework, if applicable, we extracted the main constructs (Additional file 3 ) and main phases (Additional file 4 ). The data extraction sheet was developed and piloted by two reviewers (G.L., M.G.) on two eligible papers and reviewed accordingly. D.A., K.M., J.Z., M.B., M.G., K.G. and G.L. extracted all data independently and blind-folded.

Data analysis

With the scope of aligning findings and come to a final set, extracted data was then cross-checked between G.L. and K.G. G.L. and K.G. sought consensus and, if in disagreement, they involved MG as a third reviewer. Final data was then plotted by G.L. in Fig.  1 , which includes the framework’s classification data and steps and constructs reported in Additional files  2 and 3 . K.G. and G.L. further conducted thematic analysis of included frameworks, as described below.

Thematic analysis

Through thematic analysis, we aimed to identify from included frameworks recommendations that would be relevant to the context of co-creation. This process followed the six stages as outlined by Braun and Clarke [ 58 ], including the following steps: (1) G.L. and K.G. familiarized with the data and wrote familiarization notes; (2) G.L. and K.G. developed a coding; (3) G.L. and K.G. independently generated initial themes from coded and collated data; (4) finally met to develop and reviewing themes; as well as to (5) refine, define and name themes; and (6) they applied the thematic framework to the remaining frameworks. [ 24 ]. The two researchers (G.L. and K.G.) independently coded four frameworks and then met to develop the thematic framework, which was then applied to the remaining frameworks. The themes and related recommendations reported in the results section emerged as the result of the data coding and iterative theme development [ 58 ].

Coding themes included the following: (a) early implementation considerations—exploring how the frameworks were including early implementation consideration; (b) system thinking—understand in what way frameworks were framed within a systems thinking paradigm [ 21 , 29 , 30 ]; (c) partnering with stakeholders—retrieving information on how the frameworks partnered with the stakeholders; (d) experience—how frameworks were pointing towards an assessment of the users’ experience throughout the process; or, finally, (e) iterative and cyclical evaluation—we explored how frameworks were accounting for this aspect.

Summary of identified frameworks

From a total of 9061, after removing duplicates, 5284 papers were screened at title and abstract and 425 retrieved for full-text screening. We identified 30 articles and related implementation and evaluation frameworks (Table  3 ).

As shown in Fig.  1 , among frameworks, we identified 18 process models, 11 determinant frameworks and 16 evaluation frameworks. Most frameworks (16) are implementation frameworks, including evaluation elements, while 9 frameworks exclusively look at implementation or evaluation. There are 13 descriptive and 18 prescriptive frameworks. A total of 12 frameworks include implementation considerations from the stage of intervention development. Frameworks focus on various evaluation elements, with most frameworks (21) including effectiveness and 16 looking at impact evaluation, 15 at efficacy, 17 frameworks concern process evaluation and 11 related to an evaluation of elements related to the user experience. A total of 3 evaluation frameworks were concerned with evaluation of the planning phase, 8 were concerned with the conducting phase, 11 were concerned with post-execution and 12 were used throughout all stages.

Prominent constructs and steps were extracted from selected frameworks to give an overview of constructs (Additional file 3 ) and steps (Additional file 4 ) included in and steps that made part of the frameworks. Constructs that have been mentioned in frameworks include evidence, feasibility, acceptability, etc. and steps reported were moments of the process that the framework suggested carrying out.

Thematic analysis results

Below, we share findings derived from thematic analysis. Our intention is for these insights to represent recommendations that might be relevant for future research and the implementation of co-creation in practice. Figure  2 represents key themes and sub-themes identified through the thematic analysis of the identified frameworks.

Recommendations

Early implementation considerations in extracted frameworks

A total of 12 frameworks include reference to early implementation considerations. Eslava-Schmalback et al. [ 37 ], recommend identifying critical factors for implementing equity focus recommendations and exploring barriers and facilitators of the intervention from the design phase. Kitson et al. [ 40 ] pay attention to preparing the intervention for context application, and Wimbush and Watson [ 44 ] call out the possibility of significant inconsistency between an intervention developed in experimental conditions and implementation in the real world.

Among frameworks, Cambon and Alla [ 33 ] focus on the context in which the intervention is implemented and argue that this should be viewed as an “interventional system”. In most frameworks, taking into account potential barriers to implementation takes the form of attention to the acceptability of its final users. It is claimed that by assessing its acceptance rate with users, the intervention might address potential barriers to its real-world application. In its description of the intervention cycle, Campbell et al. [ 31 ], for instance, advises adopting an iterative process through which the potential recipients’ acceptability of the intervention is assessed and re-examined if needed.

Similarly, Gonot-Schoupinsky and Garip [ 46 ] framework dedicates special attention to appropriateness and morality and how the user feels about the intervention as it may impact the intervention’s scalability potential. Assessing acceptability to both end-users and to stakeholders early in the process may be a crucial consideration for large-scale intervention implementation because of its potential to identify potential contextual barrier, enablers and motivations to participation in interventions [ 46 ].

Systems thinking in frameworks

Among the frameworks identified, seven explicitly reference a systems thinking perspective. Best et al. [ 43 ] advocate for a systems-grounded frame to be built with key stakeholders. Lo and Karn [ 41 ] view complex health programme interventions as systems composed of interdependent features and factors. The latter includes interdependent features and characteristics, such as human behaviours/perceptions, skills and capacity, and governmental and physical structures.

Similarly, Titler [ 51 ] recommends finding ways to account for systemic factors in the study design and randomized controlled designs. Zucca et al. [ 52 ] place intervention within a systems approach, making a distinction between a complex systems approach, in which variables are so intertwined that the cause and effect relation is uncertain, versus a complicated system, where numerous elements and relationships exist but their relationship can be unveiled and understood. Within the same perspective, Eslava-Schmalback et al. [ 37 ] stress the importance of understanding complex systems to advance and enhance implementation.

Partnerning with stakeholders

Co-creation is considered an approach that promotes engagement in partnership with stakeholders throughout the intervention. We reported on the level of engagement by identifying frameworks that had involved their stakeholders in a partnership, meant, according to Arnstein’s ladder, as the commitment to share planning and decision-making responsibilities through a set structure with its key stakeholders [ 53 ].

Six frameworks include elements of partnership with stakeholders. Racher and Annis [ 48 ] and Leask et al.’s [ 25 ] framework define partnership as the instance in which the stakeholders experience ownership while also (b) providing directional guidance and (c) being invested with responsibilities for activities and outcomes [ 48 ].

Partnerships are to be established according to frameworks, with different groups, including with (a) people involved in programme operations, (b) those served or affected by the programme, and (c) primary users of the evaluation [ 54 ] or, when community partnerning, between (a) multidisciplinary researchers, (b) the health researchers and community practitioners, and (c) and community health organizations at an international, national and local level [ 43 ].

Stakeholders’ views and experience are considered equal to other types of knowledge by Kitson et al. [ 40 ] Chen [ 38 ], which include patient preferences, views and experience as equally valuable and crucial to evaluate whether an intervention is practical, affordable, suitable, evaluable, and helpful in the real world.

Evaluating the experience of the co-creators

Benefits of joining the co-creation process might include cognitive, social and personal benefits [ 55 ]. To maintain and assert the value of co-creation to the co-creators involved, assessing their experience seems essential.

We reviewed the extent to which frameworks were evaluating the experience of the stakeholder’s involvement in the intervention. Gonot-Schoupinsky and Garip [ 46 ] include the assessment of acceptability to include reflections on appropriateness or morality, and how the user might have felt about the intervention, while Jolley et al. [ 50 ] suggest investigating barriers to participation and state the importance of ensuring the process is inclusive and values diversity.

Hennesy Lavery et al. [ 37 ] and Masso et al. [ 47 ], when evaluating the intervention, assess whether the level of agency of participants has increased and regard it as crucial to achieving the sustainability of the intervention. Marckmann et al. [ 35 ] stress the importance of evaluating the impact on autonomy while including the elements of health-related empowerment, such as health literacy, respect for individual autonomous choice and protection of privacy and confidentiality.

Iterative and cyclical evaluation

To comprehensively account for influential implementation elements, iterative evaluation at the planning and conducting phases allows researchers to address and prevent implementation obstacles by assessing the stakeholders’ perceptions and views and adjusting the intervention as needed.

Among frameworks, seven studies recognize the need to perform a more cyclical and iterative evaluation to allow for an intervention to be sustainable within its actual context and replicability to others [ 36 , 44 , 47 , 50 , 54 ]. Wimbush and Watson, for instance, suggest iterative evaluation as a way to review the intervention's feasibility, practicability, acceptability and for adjusting the programme’s initial design.

This review identified 30 implementation and evaluation frameworks, classified according to their types and according to the categories specified in the data extraction. By analysis the frameworks through thematic analysis, it also offered insights into considerations for when implementing and evaluating future research and practice of co-creation.

Recommendations included accounting for early implementation considerations. Anticipating implementation questions has in fact been argued to be a way to increase the sustainability and maintenance of the intervention in the real-world setting [ 42 ] and considered by Moore et al. as crucial for future intervention development and evaluation [ 28 ]. Considering the adoption of a systems thinking approach was included as a key facet. Interventions, it is argued, need to be contextualized and understood in, rather than isolated from, the systems they operate within and co-creating interventions with its relevant stakeholders and intended target population, who hold deep knowledge of the systems they are situated within, can ensure a closer tie between theory and context.

By working with multiple levels of influence and with related contextual settings and systems, systems thinking [ 56 ]  seems to fit well the scope and intention of co-creation. With its intention to map the larger environment and to identify obstacles and challenges impacting and affecting the public health matter in question, systems thinking enables co-creation to address beyond the isolated causal effect but rather to explore and identify the multiplicity of real-world systematic factors that collate and contribute to the complex problem.

Evaluation is essential and crucial to co-creation and key is formative and cyclical evaluation, as suggested by Anneke van Dijk-de Vries et al. [ 22 ]. This, in fact, allows researchers to address and prevent implementation obstacles by assessing the stakeholders’ perceptions and views and adjusting the intervention as needed throughout.

By fostering reflection moments to ensure that end users’ perceptions are continuously captured [ 25 ], iterative process evaluation can represent a powerful tool in placing the voice and perception of the co-creators at the core of the intervention cycle. Doing so is particularly relevant when co-creating, as the process and how the co-creators are involved throughout, become part of a co-created intervention’s major outcomes and value in itself  [ 22 ].

Partnering with stakeholders and evaluating the co-creators experience is key as the co-created solution is expected to be developed jointly and provide benefits to the co-creators. Valuing the co-creators’ perceived level of co-ownership has been previously regarded essential to the co-creation process [ 25 ] and a way to ensure the co-created solution is developed through meaningful engagement.

This review identifies 30 implementation and evaluation frameworks for co-creation and offers recommendations for the planning and evaluating of co-creation for public health. Recommendations emphasize the importance of early implementation considerations, adopting a systems thinking approach, and prioritizing formative and cyclical evaluation. Iterative process evaluation is suggested as a powerful tool to centre co-creators’ voices in intervention cycles, posing and recognizing the value of the co-creation process itself, and not only on the implementation of the co-created solution. To underscore the significance of meaningful engagement and co-ownership when developing co-created solutions, the review highlights attention on the partnering with stakeholders and on an evaluation of the co-creators’ experiences.

This scoping review is conducted as part of the Health CASCADE study and findings will be used to inform the development of further guidance on planning and evaluating co-creation for public health. Authors will dive deeper into the framework by Leask et al. 2019 [ 25 ] identified through this review to identify strengths and weakness and expand on the available guidance, through a scoping review, and qualitative interviews with key stakeholders. Authors will also conduct a scoping review on process evaluation studies for co-creation and qualitative interviews with key stakeholders to develop an evaluation framework for co-creation.

Limitations of the study

Firstly, as we intended to explore the broader implementation field, we included several types of implementation frameworks within our definitions. This meant we captured several non-primary studies, presenting frameworks that had been developed for and/or applied to specific settings and contexts. These studies were later excluded at full-text screening. This might have caused the loss of frameworks that were specific to a context and setting but relevant to the scope of the study. We, however, as previously explained, performed snowballing on the identified frameworks to reduce this possible loss.

Secondly, while we set the search strategy with a specialist librarian, the review might have missed terms used for the same scope by other professionals (e.g. reporting guidelines, checklist or step-by-step how-to).

As part of our analysis, we scoped for implementation intervention development frameworks and did not include public health intervention development frameworks as we were interested in frameworks built to help guide the implementation of the intervention. Therefore this means the search lacks frameworks supporting the intervention development. We applied the search strategy to databases focussing on public health per the review’s scope. Therefore, this review might lack frameworks in databases from social sciences, although the snowballing exercise aimed to reduce the bias as it was performed with no limitations to the databases’ field. It is also worth acknowledging that the frameworks’ classification and data extraction were extracted independently by two reviewers and agreed upon by consensus to ensure the analysis was accurate. Nevertheless, interpretations made as part of the frameworks’ analysis were based on the reviewers’ subjective appraisal.

Conclusions

This review identified, classified and analysed 30 implementation and evaluation frameworks and offered recommendations for professional stakeholders and researchers wanting to adopt a co-creation approach.

The study recommends, when co-creating, to (a) include implementation considerations from an early stage and at the stage of intervention planning, (b) adopt a systems thinking approach when co-creating, and (c) form a partnership relationship with stakeholders to (d) plan for an iterative and cyclical evaluation and (e) focus on evaluating the co-creators’ experiences.

Contributions to literature

This scoping review identifies and classifies 30 implementation and evaluation frameworks for the development, implementation and evaluation of interventions in public health.

The analysis suggests positioning implementation considerations from an early start of the intervention and adopting a systems thinking approach to the implementation and evaluation of co-created interventions.

The authors highlight the importance of partnering with stakeholders and recommend carrying out an evaluation that is iterative and cyclical and focusses on the experience of the co-creators.

Availability of data and materials

Further data and materials are included in the additional files.

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Diverse adolescents’ transcendent thinking predicts young adult psychosocial outcomes via brain network development

• Rebecca J. M. Gotlieb 1   na1 ,
• Xiao-Fei Yang 2   na1 &
• Mary Helen Immordino-Yang 2 , 3  

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Developmental scientists have long described mid-adolescents’ emerging capacities to make deep meaning about the social world and self, here called transcendent thinking, as a hallmark developmental stage. In this 5-years longitudinal study, sixty-five 14–18 years-old youths’ proclivities to grapple psychologically with the ethical, systems-level and personal implications of social stories, predicted future increases in the coordination of two key brain networks: the default-mode network, involved in reflective, autobiographical and free-form thinking, and the executive control network, involved in effortful, focused thinking; findings were independent of IQ, ethnicity, and socioeconomic background. This neural development predicted late-adolescent identity development, which predicted young-adult self-liking and relationship satisfaction, in a developmental cascade. The findings reveal a novel predictor of mid-adolescents’ neural development, and suggest the importance of attending to adolescents’ proclivities to engage agentically with complex perspectives and emotions on the social and personal relevance of issues, such as through civically minded educational approaches.

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Introduction

Adolescence is a period of marked cognitive, emotional and psychosocial growth 1 , as well as a sensitive period for neurological development 2 , the second such period after infancy. It is characterized by sensitivity to the social context and by the emergence of increasingly sophisticated abilities to interpret the social world and react with complex emotions to its happenings 3 , 4 . By middle adolescence, from approximately 14–18 years of age, youth develop the capacity for “transcendent” thinking. That is, mid-adolescents are disposed, and often motivated, to enrich their concrete, empathic, and context-specific interpretations with abstract, systems-level considerations that transcend the current situation 5 , 6 , 7 , 8 , 9 . They invoke broader perspectives on themselves, other people, and social systems, and draw on cultural values and associated emotions to infer social and ethical implications and build deeper understandings 10 , 11 . Moving into the later teenage years, transcendent thinking supports late-adolescents’ identity development, the process of building self-definitions rooted in reflections on experiences, hopes, relationships, values, and beliefs rather than on happenstance. As such, transcendent thinking may contribute to stronger identity achievement and less identity diffusion 12 , 13 , 14 . The identity development process can support a healthy transition to young adulthood 15 , 16 , 17 , in the early twenties, with emotionally fulfilling and stable relationships, a positive sense of self and life purpose 18 , and productive, ethical use of educational and work opportunities 19 , 20 . Especially among ethnically diverse youth, and youth from families of low-socioeconomic circumstances, transcendent social thinking and identity are important developmental assets, given the likelihood these youth will face complex circumstances and social challenges 11 , 21 .

Research in developmental science and education has long documented the academic and social benefits of supporting adolescents in building intellectual agency and developmentally appropriate capacities for thinking about complex social, civic, and academic disciplinary content 11 , 19 , 22 , 23 . Curiously, though, the affordances of mid-adolescents’ transcendent thinking for brain development have not been investigated. Neural maturation across mid-adolescence largely involves increasingly efficient communication among characteristic networks contributing to a range of developing psychological capacities 24 , 25 , 26 . This increasing efficiency is measurable in neural dynamics, i.e., in the real-time correlation between different neural networks’ activity fluctuations, even as individuals rest 24 , 27 . The change over time in the strength of these correlations, therefore, can be used as a metric of functional neural development. Across adolescence, there are considerable individual differences in these metrics. As these differences correlate with psychosocial functioning and mental health 25 , 27 , 28 , 29 , 30 , it is important to understand their origins 31 .

A previous study of ours revealed that mid-adolescents’ transcendent construals of social stories were associated with increased default mode network (DMN) activity, especially when the adolescent reported feeling strongly emotionally engaged with the story, and with decreased executive control network (ECN) activity 32 . In addition, the association between DMN activity and transcendence was strengthened by brief ECN activity early in the trial. These complex findings suggested that adolescents’ transcendent construals involve coordinated activity of the DMN, which supports internally generated reflections and prospections 33 , 34 , 35 , and the ECN, which supports goal-directed thinking and focused attention 36 , 37 . This interpretation is consistent with previous theoretical and empirical work documenting these networks’ coordinated involvement in many forms of creative, episodic, social-emotional and generative thinking 38 , 39 , 40 , 41 , 42 , and with research documenting links between maturation of these networks and social-emotional, cognitive and executive functioning and mental health 43 , 44 , 45 , 46 . Might the developmentally characteristic recruitment of transcendent thinking across mid-adolescence contribute to organizing connectivity between these two networks in ways that support long-term psychosocial growth and well-being?

Brain maturation during mid-adolescence is thought to reflect, in part, social and cognitive experiences, such as effects of socioeconomic and cultural backgrounds, education, and peer influences 4 , 47 , 48 . However, whereas exposure to social circumstances is an important source of experience, equally important may be adolescents’ growing propensities to grapple psychologically with what they witness, and make meaning 49 . Although IQ and demographic variables such as socioeconomic status (SES) have been associated with neurodevelopmental trajectories across childhood and early adolescence 46 , 50 , 51 , it is possible that their effect on longitudinal change across mid-adolescence may attenuate. Potentially, proclivities to expend effort on complex thinking may come to play a more prominent role.

To begin to investigate how mid-adolescents’ propensities for transcendent social-emotional thinking may predict subsequent brain development, with potential psychosocial implications in young adulthood, we launched a longitudinal 5-years study involving sixty-five 14–18 years-old youth of color from low-income urban communities. There is an urgent need in the psychological and brain sciences to study populations that have not traditionally been involved in research, i.e., non-White and low-to-mid SES samples, and in particular to focus on these populations’ normative development, rather than solely on deficits and risks 52 . At the time of recruitment, participants were healthy mid-adolescents from stable families, passing all classes at school, and not under disciplinary action. Our sample is diverse; participants speak English and were attending U.S. public high schools, but their parents had immigrated to the United States from thirteen different countries, primarily in East Asia and Latin America. Asian and Latinx youth are substantial and growing segments of the U.S. population. Our sample also is reflective of the variation in parental education levels and financial circumstances that exists within low-SES communities.

Participants explained their reactions to compelling mini documentaries in 2-h private interviews that were videotaped, transcribed, and coded for transcendent construals. Our interview provided participants with interesting, emotionally compelling true stories and a socially supportive situation conducive to reflection, and then allowed them to respond freely. Participants also underwent resting-state fMRI at the beginning of the study and after 2 years, to capture the longitudinal change in DMN and ECN internetwork connectivity. Identity development was surveyed after 1.5 more years, in late adolescence. In young adulthood, 5 years after initial data collection, when participants were in their early twenties, participants rated their satisfaction with self, relationships, and school to capture psychosocial well-being. We hypothesized that mid-adolescents’ construction of transcendent construals in the interview at the start of the study would predict increases in the DMN and ECN networks’ interconnectivity over the subsequent 2 years, regardless of the initial level of interconnectivity between these networks. We further hypothesized a sequenced developmental cascade in which the increases in these networks’ interconnectivity would in turn positively predict late-adolescent identity development, which would predict self and relationship satisfaction in young adulthood. To differentiate effects of transcendent thinking on brain network development from effects of age, family financial status and parents’ education levels (measures of SES), intelligence as measured by IQ, and other personal characteristics and demographic variables, we also measured, analyzed, and then controlled for these factors.

Every participant produced transcendent construals over the course of the interview at the start of the study (M = 25.14, SD  = 14.00, range 2–64). Transcendent construal scores were not significantly related to IQ ( r [59.9] = 0.23, p  = 0.07, 95% CI [− 0.02, 0.45]), age ( r [63] = 0.22, p  = 0.08, 95% CI [− 0.02, 0.44]), or other demographic covariates (SES [family income/needs ratio and parents’ years of education], sex, ethnic background; all p’s  > 0.44).

Transcendent construal scores predict increases in internetwork connectivity over time

Consistent with our hypothesis, adolescents’ transcendent construal scores predicted the increase in connectivity between the DMN and the left ECN components across the 2-years interval following the interview, controlling for differences in head motion between the two neuroimaging data collections and time between data collections ( b  = 0.007, SE  = 0.003, t [55.9] = 2.70, p  = 0.009; bootstrapped 95% CI [0.002, 0.012]). Results hold in a model additionally controlling for age, sex, IQ, SES and starting level of connectivity between these components ( b  = 0.005, SE  = 0.002, t [49.2] = 2.26, p  = 0.03; bootstrapped 95% CI [0.0005, 0.0094]). The effect of transcendent construals on growth in internetwork connectivity was not significantly moderated by age, sex, IQ or SES (all p’s  > 0.44).

Ethnic background

Given current discussions about the generalizability of psychological effects across ethnically diverse samples 52 , 53 , the effect of transcendent construals on longitudinal change in DMN and left ECN interconnectivity was examined in a model with participants divided into two broad ethnic groups: East-Asian descent ( n  = 29) and Latinx/Afro-Latinx descent ( n  = 36). The effect of transcendent construals holds ( p  = 0.008). Ethnic group did not moderate the effect of transcendent construals on change in internetwork connectivity ( p  = 0.71).

Results were lateralized to the left ECN. Change in connectivity between the DMN and right ECN components across the 2-years interval was not predicted by transcendent construal scores, controlling for difference in head motion between the two data collections and time between data collections, p  = 0.82.

The sequenced developmental cascade from transcendent construals in mid-adolescence to young adult life satisfaction

Psychosocial outcomes varied across participants (identity development, a composite measure of identity achievement and diffusion: M  = 3.69 out of 5, SE  = 0.09; life satisfaction, a composite measure of satisfaction with self, various social relationships, and school [for the 49 still attending school]: M z-score = 0.05, SE  = 0.11).

Developmental measures were chronologically ordered, and a series of regression models revealed significant effects from one to the next. A path analysis then revealed that the complete path is significant, while alternative paths that omit either or both of the intermediate measures are not. The findings together suggest a developmental cascade; see Fig.  1 . All models control for differences in head motion between the two scans and time between scans.

The longitudinal path from transcendent construals in mid-adolescence to life satisfaction in young adulthood, through 2-year change in resting-state internetwork connectivity (Δ Connectivity), and identity development in late adolescence. Consistent with a developmental cascade, only the complete path (black arrows) is significant; see inset. Alternative paths that omit either (blue arrows, red arrows) or both (purple arrow) of the intermediate measures are not significant. Regression coefficients are depicted for the complete path, with standard errors in parentheses. DMN, default mode network; ECN, executive control network; ** p  < 0.01, * p  < 0.05, slashes signify a non-significant relationship; CI, confidence interval.

For at least a century, developmental theorists have described adolescents’ emerging abilities for transcendent social thinking, known also as abstract thinking, as a hallmark developmental stage 6 , 8 , 12 , 54 . Here, we demonstrate that adolescents’ proclivity to engage with such thinking predicts key, large-scale brain networks’ increasing interconnectivity over time and that this neural development is, in turn, associated with personal and social well-being in young adulthood. Importantly, in our socioeconomically and ethnically diverse urban sample, IQ and demographics did not explain the findings.

We focused on the default mode and executive control networks in particular because we had previously demonstrated that mid-adolescents’ transcendent construals were associated with coordinated activity in these networks during a functional task 32 . Extensive research suggests that these networks support reflective, autobiographical and free-form thinking, and effortful, focused thinking, respectively 34 , 35 , 36 . These networks’ coordinated activity is associated with many forms of generative and social-emotional processing 38 , 39 , 40 , 41 , and with mental health, including among adolescents 45 . Our study associates the positive developmental coordination of these networks with mid-adolescents’ emerging dispositions to construct transcendent meaning, inferring the broader, systems-level, ethical and civic implications, and emotionally poignant values and personal lessons, that extend beyond the immediate social situation.

Our study utilized an open-ended interview approach that aimed to capture the patterns of thinking participants spontaneously employed as they reacted to compelling, true social stories. There is a long history in constructivist developmental science of examining individuals’ processes of thinking independent of the specific content; this approach allows researchers to capture developmentally characteristic ways of thinking 6 , 8 , 12 , 55 . Building from this tradition, we analyzed not “what” youth were thinking, whether they agreed with the protagonist’s choices or the values the protagonist appeared to endorse, but “how” they were thinking, whether they showed evidence of considering the broader implications of the story for themselves or the world. Similarly, identity development measures captured the degree to which a participant had deliberated on their values and views, without regard to what they had decided. Notably, every participant produced transcendent construals during the interview, and therefore was capable of transcendent thinking. Given this finding, our method arguably assesses adolescents’ developing agentic dispositions toward transcendent thinking—how much they spontaneously invoke this cognitive-affective process, given a situation or domain that invites it. Ultimately, transcendent thinking may be to the adolescent mind and brain what exercise is to the body: most people can exercise, but only those who do will reap the benefits.

Our findings also speak to the value of utilizing in-depth qualitative research in developmental cognitive neuroscience to begin to understand meaningful sources of individual variation in brain development, and meaningful implications of this variation for outcomes. Our modest sample size was chosen to make possible a natural-feeling experimental protocol that encourages participants to engage genuinely with the true stories we shared, and to feel comfortable taking the time to figure out and explain their responses. Although future research might possibly design a more efficient means of uncovering adolescents’ psychological propensities, the ecological validity intended in our approach allowed us to uncover a critical interindividual difference in intraindividual change 56 . The focus on large-scale neural networks allowed us to align a broad psychological capacity with an equivalently broad neurodevelopmental pattern, facilitating interdisciplinary interpretation of the findings 57 . The data provide a developmental cascade that is consistent with theoretical accounts of self-construction within the dynamic developmental system 8 , i.e., with the notion that youth actively contribute to their own development 23 . Our previous reports that, at the start of the study, participants’ patterns of construals were associated with real-world social and cognitive functioning 7 , and with trial-by-trial activity in the brain 32 , strengthen this interpretation.

Related, the findings speak to the power of longitudinal designs for understanding the ontogeny of interindividual variation in outcomes, and possible targets for intervention. By focusing on participants’ neural connectivity change scores , embedded in a longitudinal path of characteristic psychosocial developmental achievements—we are able to learn about a trajectory of neural change that is associated with later psychosocial health, and about a psychological precursor of that change. The change-score logic is similar to that of pediatricians who record infants’ eating and weight across time not because they are primarily interested in how a baby’s weight compares to that of their same-age peers (assuming the baby is within a normal weight range), but because they aim to describe the infant’s rate of weight gain relative to that of their peers. It is this rate that is associated with health outcomes. Pediatricians would also advise parents about the modifiable factors that effect the rate of weight gain, such as feeding. Analogously, our study found that neural change over time was associated with later psychosocial development indicative of young adult psychosocial health, and that transcendent thinking predicts this neural change. This finding underscores the importance of the developmental process cascading from transcendent thinking, and points the way for future studies that would strategically assess whether and when interventions could be effective.

Future research should also focus on the developmental origins of individual variation in mid-adolescents’ transcendent thinking, especially as transcendent thinking is context dependent and appears to be malleable over time 9 . Given adolescents’ expanding social sphere, our data suggest the possibility that adolescents’ emerging disposition for thinking transcendently about what they encounter may itself be a source of variation in how the brain develops over time, akin to the association between eating well and growing well as an infant. Schooling, community-based programming, and parenting that support adolescents’ capacities to generate culturally relevant meaning of their social world, and to build identity, produce lasting benefits 23 . These can include improved academic performance and persistence in school 19 , 21 , 58 , 59 , increased sense of life purpose 9 , and improved biological markers of health 60 .

Further, future studies linking developmental origins of transcendent thinking to earlier brain development and social circumstances might possibly contribute to sorting out aspects of the trajectory of neural development across adolescence. In general, more protracted periods of structural development, and higher levels of internetwork connectivity and neural network segregation, have been associated with beneficial life circumstances and psychological outcomes, as indexed mainly by SES and IQ 51 , 61 . However, while neural network integration generally increases across this age 24 , research on the relations between SES and functional neural development of resting state network connectivity in adolescence has produced conflicting findings 51 , 62 , 63 . The relative lack of clarity on these topics may reflect in part the complex dynamics of thought and emotion emerging at this age, and in part the limitations of the predictive constructs. In particular, measures of SES and IQ are coming increasingly under scrutiny for their potential cultural biases and, in the case of SES, for their inability to capture critical variation in youths’ family and community-level social supports and cultural assets that may facilitate patterns of thinking like those we capture here 64 , 65 , 66 . Future research should investigate the value of more naturalistic, ecological methods for studying the psychological correlates and predictors of brain development, and especially of methods that capture youths’ strengths and not simply their environmental liabilities.

From a more applied perspective, future research should test the causal nature of the relationship between transcendent thinking and future neural and psychosocial development, and the ways that exposure to educational and clinical practices designed to support increases in transcendent thinking may contribute to future growth. For example, research on the possible neurobiological effects of civically-oriented community schooling 19 and restorative justice approaches 59 , 67 could lead to insights for developmental science while contributing useful evidence for education, mental health, and juvenile justice reform 22 , 57 . Examining the ways that adolescents’ transcendent thinking can be leveraged for both prosocial and antisocial aims, toward healthy and unhealthy outcomes, is another important future direction. Our assessment of transcendent construals was value neutral; we did not judge the prosociality or normativity of a participant’s response. (That said, we note that our participants were screened for psychiatric diagnoses and serious disciplinary infractions, and their transcendent construals were overwhelmingly prosocial.)

We hope that our findings provide a source of developmental hypotheses for larger, longer-term longitudinal studies with the power to examine more nuanced neurological and psychological effects across a wide range of participants 68 , and normative cross-sectional ranges of neural connectivity and transcendent thinking at different ages. Ongoing research is identifying subtle patterns of longitudinal integration and differentiation in neural network functioning 27 , 30 , 47 , 69 . Many of the regions changing with development contribute to the cognitive and affective processing undergirding transcendent thinking and its components, such as emotional feelings, autobiographical memory, motivation and reward processing, and self-processing 28 , 48 , 70 . Youth will almost certainly vary on the kind of transcendent thinking processes they preferentially invoke, e.g., self-relevant versus systems-oriented, and therefore on their relative reliance on the different affective and cognitive component processes. Larger studies would be positioned to explore developmental effects with greater granularity, in youth exposed to a range of social, cultural and educational contexts, and to probe connections to various domains of information processing not explicitly social 57 . Larger studies would also be positioned to investigate the possibility of contextual moderation effects that were not detectable in our moderately-sized sample.

It is hard to imagine a human context in which the capacity to engage in transcendent thinking would not confer benefits, assuming that we collectively aim for wellness and an ethical society capable of interrogating structures and systems, and of innovation. By middle adolescence, youth are oriented to, and even agentically dedicated to, engaging in such thinking. As a result, they can count among society’s most idealistic and committed citizens. The disposition to build complex, values-based inferences about the personal, social and ethical implications of the situations we encounter, and to become curious about the reasoning behind complex societal systems, is uniquely human. The proclivity to think about issues and beliefs that transcend proximal goals and the current context is the basis for adult-like moral values, identity development, civic participation and a sense of purpose 18 . Our study suggests that as mid-adolescents engage in transcendent thinking, trying on their newly expanding capacities for making meaning, they coordinate neural networks involved in effortful thinking and internal reflection. This spontaneous, active coordination across development may contribute to the growth of both their brains and their minds, lifting them over the threshold to productive young adulthood.

Materials and methods

These data were collected as part of a larger project, for which participants also completed psychosocial activities, psychophysiological recordings and neuroimaging unrelated to the present study (e.g., interviews about school; studies of heart-rate variability; diffusion tensor imaging; see https://osf.io/gqs34 for more information). Methods for analyzing participants’ interview responses are described extensively in Gotlieb et al. 7 . The current study is the first to report longitudinal findings, and the first to analyze resting-state network connectivity and psychosocial outcome data. All study activities were approved by and carried out in accordance with the policies of the Institutional Review Board of the University of Southern California (UP-12-00206). All parents/legal guardians and participants gave written informed consent or assent as appropriate, and all participants were compensated for their time.

Participants

65 youth (36 female) were recruited from public high schools in low-SES neighborhoods in Los Angeles. All participants were right-handed, aged between 14 and 18 years at the time of the initial data collection, fluent in English and passing all classes in school; none were under school disciplinary action. None had a history of drug/alcohol use or neurological/psychiatric issues. Participant characteristics are as reported by participants and as confirmed by parents/legal guardians and teachers (to the best of their knowledge). Characteristics of the sample were as follows: 51 participants reported receiving free or reduced-price lunch at school (indicating low income/needs ratio for the family 71 , and therefore low-SES). Parents’ education, an additional factor associated with SES 50 , ranged from 8 to 18 years ( M  = 12.4 years, SD  = 3.8). 34 participants identified as Latinx, 29 as East Asian, and 2 as Afro-Latino; participants’ parents were born in 13 countries. Participants ranged in age from 14 to 18 ( M  = 15.77 years, SD  = 1.05) at the start of the study. IQ scores ranged from 79 to 131 ( M  = 103.6, SE  = 1.52; see below for relevant methodological details).

Adapting a previously established protocol (see Immordino-Yang et al. 72 ), participants reacted to 40 true, compelling stories about living, non-famous adolescents from around the world in a range of circumstances, during a 2-h private video-taped interview. The story corpus was previously piloted to be interesting and to elicit mixes of positively and negatively valenced emotions. The experimenter shared each story using a previously memorized script, and then played an accompanying documentary-style video of approximately 1 min in length depicting footage of the real-life protagonist (not an actor), using PowerPoint (Microsoft Office) displayed on a Lenovo laptop with a 17-inch screen. After showing each video, the experimenter asked, “how does this story make you feel?” The experimenter then looked down and transcribed as much as possible of the participant’s verbatim responses by hand-written notes. Participants were told that notetaking was conducted in case the video camera failed. Actually, these notes also served to standardize the experimenter’s behavior, so that the participant could respond freely. Participants were encouraged to be as candid as possible.

Neuroimaging

Following the interview and a short break, participants underwent a 7-min resting-state BOLD fMRI scan with simultaneous pulse monitoring. Participants were instructed to think about whatever they would like, to stay as still as possible, and to stay awake. An image of a nature scene without people or animals was displayed continuously. Participants returned to the lab to repeat the scan approximately 2 years later ( M time between scans  = 2.10 years, SD  = 0.21, range = 1.94–3.28 years). The protocol was timed so that scans would occur in the middle of the day.

MRI data acquisition

BOLD fMRI scanning at initial data collection was conducted with a 3 Tesla Siemens Trio scanner and a 12-channel matrix head coil. Functional scans were acquired using a T2 ∗ -weighted echo-planar imaging (EPI) sequence (TR = 2 s, TE = 25 ms, flip angle = 90°, acquisition matrix: 64 × 64, FOV = 192 mm) with a voxel resolution of 3 × 3 × 3 mm 3 . Forty-one continuous transverse slices were acquired in interleaved order to cover the whole brain. A total of 210 volumes were acquired during the 7-min resting state scan. Anatomical images were acquired using a magnetization-prepared rapid acquisition gradient echo (MPRAGE) sequence (TI = 800 ms, TR = 2530 ms, TE = 3.09 ms, flip angle = 10º°, isotropic voxel resolution of 1 mm 3 ; acquisition dimensions: 256 × 256 × 176). At the second data collection, a 3 Tesla Siemens Prisma scanner with 20-channel matrix head coils was used due to a system upgrade at the scanning facility. Scanning parameters remained the same. (N.B.: the analysis focuses on interindividual effects, which would be independent from any effects of the scanner upgrade.)

Pulse oximetry data acquisition

Pulse oximetry was acquired using an MRI-compatible oximeter (Nonin Medical Inc, 8600FO MRI, Plymouth, MN, USA) placed over the middle finger of the participant’s left hand; data were output to a BIOPAC MP150 system and recorded using the BIOPAC Acqknowledge software (version 4.1; BIOPAC Systems Inc., Goleta, CA, USA).

After the second fMRI scan, a trained experimenter individually administered the vocabulary and matrix reasoning subtests of the Wechsler Abbreviated Scale of Intelligence, second edition 73 in a private room. Subtests were scored, age normed and summed to produce total IQ scores. One participant completed only the vocabulary subtest due to time constraints; we imputed overall IQ from this score.

Psychosocial survey measures

Approximately 3 years, 4 months after the initial data collection, participants completed a modified and abridged version of the Objective Measure of Ego Identity Status instrument 74 , 75 via an online survey that they received via electronic communication (i.e., email, text message, and/or social media direct message). Three questions measured identity achievement (“I have gone through a period of serious questions about my values”; “I have developed my own viewpoint on what is best for me”; “I engage in self-exploration and discussions with others to figure out my views on life”). Two questions measured identity diffusion (“I just hang with the crowd”; “I sometimes join activities when asked, but I rarely try anything on my own”). All used a 5-point Likert scale, from “not at all true” to “completely true.” A composite identity development score was calculated from the average of responses on the Identity achievement questions and identity diffusion questions (reverse scored).

Approximately 5 years after initial data collection, participants completed an online survey of life satisfaction to capture well-being. Participants reported their satisfaction with their social relationships (as many as pertain; 7-point Likert scale; one question each for parents, siblings, friends, teachers/supervisors, romantic partner, children), with themselves (continuous sliding scale; “how satisfied are you with whom you have become?”), and with school (only if relevant; continuous sliding scale; “how much do you like school?”). A composite life-satisfaction score was calculated from the average of z -scores for: satisfaction with self; satisfaction with school (if relevant); and satisfaction with social relationships (calculated as an average of reported values). All survey instruments were administered using Qualtrics software (Provo, UT).

Transcendent construals

For our previous study, videotaped interviews had been transcribed and verified. Each participant response had then been blind coded and reliability coded for transcendent construals (See Gotlieb et al. 7 , 32 ). Building from our previous work, transcendent construals were defined as utterances reflecting:

(i) systems-level analyses or moral judgements, or curiosities about how and why systems work as they do, e.g.,

“I also find it unfair that the people get undocumented. It’s kind of weird how it’s like a label how like just ‘cause you are from some other place, um, you can’t do certain things in another place. It’s like a question. It’s like something I’ve always wondered…”;

(ii) discussions of broad implications, morals and moral emotions, perspectives, personal lessons or values derived from the story, e.g.,

“I think back to the idea that because children are the future […] we have to be able to inspire people who are growing and have the potential to improve the societies”; “it makes me happy for humanity”;

or (iii) analyses of the protagonist’s qualities of character, mind, or perspective, e.g.,

“[she is] thinking, ‘oh, you’re not alone. You have others who are dependent on you’.”

Importantly, it was not relevant whether the participant endorsed a value or lesson or agreed with the protagonist, e.g.,

“I wouldn’t react that way. I’d just be really mad at the kid instead of, you know, selfless like that and trying to help him. Like I wouldn’t be able to put myself in someone’s shoes like that like he did.”

Construals not considered transcendent pertain mainly to discussions of the protagonist’s immediate situation, e.g., “I’m glad it all worked out,” or evaluating the protagonist’s decisions or actions, e.g., “I feel like they should have planned it more”; or to the empathic emotions of the participant, e.g., “I feel really sad for her, and like, second-hand embarrassment”. Unlike transcendent construals, these examples involve reactive, concrete and context dependent interpretations.

Participants received a score of 1 for each transcendent construal. Scores across all trials were summed to produce a total score for each participant.

Neural data processing

Fmri data pre-processing.

MRI data underwent standard preprocessing using SPM12 (v.7771) implemented in MATLAB 2015b (Wellcome Department of Cognitive Neurology, London, UK; MathWorks, Inc., Natick, MA, USA). Functional images were slice timing and motion corrected, and co-registered to the anatomical image. Anatomical images were normalized to the Montreal Neurological Institute space using the segmentation procedure. The resulting normalization transformation was applied to the functional images. Co-registered and normalized images were visually inspected for each participant to ensure quality, and all were satisfactory. The functional images were resampled into an isotropic voxel resolution of 2 × 2 × 2 mm 3 and smoothed using an 8 mm full width at half maximum Gaussian kernel. To quantify and evaluate head motion, framewise displacement (FD 76 ) was calculated. Across all scans, the number of volumes across the scan with FD over 1 mm ranged from 0 to 63 out of 210 (M = 4.0, SD = 8.6); the average FD across the resting state scan ranged from 0.06 to 0.92 mm (M = 0.19, SD = 0.13). No data were discarded due to head motion, though special care was taken to ensure that network identification and the associated connectivity measures are not biased by motion, either of the head or due to cardiac pulsation; see SI Sect.  1 .

Neural network identification

Resting-state data were run through a 20-component group-level spatial independent component analysis 77 using the Infomax algorithm (as implemented in the GIFT toolbox version 4.0b, http://mialab.mrn.org/software/gift/index.html ). Group-level ICA was run without additional denoising because this approach has been shown to identify the networks of interest most accurately 78 . The 20-component model was chosen because it has been shown to capture large-scale networks, including those of interest here 79 , 80 . The first 5 TRs of the resting state scan were excluded to allow for signal stabilization. Consistent with standard practice, the algorithm was run 20 times with different initial values to evaluate the reliability of results using the ICASSO 81 function in the GIFT toolbox. All components were highly reliable, with stability indices greater than 0.96 81 . Group-level components were then back-reconstructed to create individual-level component spatial maps and corresponding component time-courses for the initial and second data collections. Component spatial maps were visually inspected; components containing the left ECN, right ECN, and the DMN, were identified (see Fig.  2 and SI Sect.  2 ). Cross-correlation values between the identified network component maps and the resting state templates from Smith et al. (2009) 80 were calculated using the fslcc function from the FMRIB Software Library (Version 6.0.6.5). Cross-correlation values for the group-level components are DMN: 0.77; left ECN: 0.74; right ECN: 0.72, considered high 82 .

Depiction of coronal, sagittal, and axial views of the group-level default mode network (DMN; top) and left executive control network (left ECN; bottom) maps, derived from a 20-component group independent component analysis of the resting-state data (concatenating data from all participants from the two data collections), transformed into z -score maps and thresholded at z  = 2.

Network functional connectivity

Network functional connectivity was calculated using the Mancovan toolbox 83 implemented in GIFT. Default corrections and, for additional confidence, motion corrections, were applied for the ECN and DMN component time courses. This step included linear, quadratic, and cubic detrending; de-spiking; low-pass filtering with a high frequency cutoff of 0.15 Hz; and, to remove any residual influence of head motion, regressing out twenty-four expanded motion parameters 84 . Pairwise between-network correlation coefficients were calculated using corrected time courses across the entire scan (205 TRs) and then Fisher z-transformed to capture the strength of functional connectivity between networks for the initial data collection and second data collection separately for each individual. To capture longitudinal change, the difference in between-network correlation coefficients for the initial and second data collections for each participant was calculated.

Statistical analysis

Statistical analyses were carried out using RStudio (Version 2023.06.0 + 421, Posit Software, PBC) and R (Version 4.3.1). All reported statistical tests are 2-tailed. Data and R scripts are available at: https://osf.io/6cejy .

Missing data and multiple imputation

All participants had complete data from the initial collection. Missing neuroimaging data at the second data collection were due to unexplained extensive signal loss (1 participant), acquiring dental braces or metal implants (5 participants), or moving away (4 participants). Only two participants attrited after completing the initial data collections; all other missing data were partial. The percentage of missing values across the variables of interest varied between 0 and 15%. Given the reasons for the missing data are known and unlikely to be related to the measures of interest, the data were assumed to be missing at random and appropriate for multiple imputation 85 . (See also sensitivity analyses, described below.)

Missing data were imputed under fully conditional specification using predictive mean matching with 10 maximum iterations, as implemented in the Multivariate Imputation by Chained Equations (“mice”) package 86  (Version 3.16). All existing data, including measures of interest and covariates, were used to conduct imputation. To stabilize results, 100 imputed datasets were produced. All difference scores were calculated after imputation.

Calculating bootstrapped confidence intervals

5000 bootstrapped samples were generated using the Bootstrap Functions (“boot”) package 87 (Version 1.3–28.1) from each of the 100 imputed datasets, following Wu & Jia’s method 88 for combining bootstrapping with multiple imputation. Effects of interest were estimated using each imputed/bootstrapped sample. The resulting 100 sets of 5000 parameter estimates were combined into one distribution for each effect of interest, which was used to derive the mean and a 95% confidence interval using the percentile method 89 .

Testing hypothesis 1

The effect of transcendent construals on longitudinal changes in between-network functional connectivity was examined using a series of fixed effect linear regression models to examine the hypothesized effect and then to confirm the effect when including additional relevant control and moderation terms. All analyses control for differences in average FD (head motion) between the two scans and time between scans. Models were run based on each imputed dataset. Two methods for significance testing were utilized to assure robustness. In the first, statistics of interest were pooled using Rubin’s rules 85 for averaging regression coefficients, combining associated variances, and calculating degrees of freedom and an associated p value (as implemented in the “mice” package 86 ). In the second, all models were estimated again using each of the imputed/bootstrapped samples to provide a confidence interval as described above.

Testing hypothesis 2

The effect of transcendent construals on life satisfaction through internetwork connectivity change and identity development, and through alternative paths that omit either or both of these intermediate measures, was tested. To do this, relevant measures were chronologically ordered to construct a developmental path model. Then, following the method described in Hayes 90 , for each of the imputed/bootstrapped datasets, three regression models were estimated using ordinary least squares, structured such that each subsequent measure in the path is predicted by the previous measures. These regression models controlled for differences in average FD (head motion) between the two scans and time between scans. Next, effects through the four possible paths (represented by the colors in Fig.  1 ) were calculated as the product of regression coefficients along the path. Significance testing was carried out based on the distribution of resulting parameter estimates, as described above.

Sensitivity analyses

Several analyses were run to give assurance that the findings are not biased by methodological decisions. To confirm that the findings hold in the sample without imputing missing data, the analyses were run with only complete cases. All findings hold; see SI Sect.  3 .

To address the possibility that the missing psychosocial data could have violated the missing-at-random assumption, we used the post-processing procedure from the “mice” package 86  to systematically vary imputed datapoints from what they would be under the missing-at-random assumption, to values corresponding to plus/minus 20% of the range of existing data. All findings hold.

Data availability

The data that support the findings of this study are available at https://osf.io/6cejy .

Code availability

The R scripts for statistical analyses presented in the paper are available at https://osf.io/6cejy .

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Acknowledgements

We thank L.G. Cardona, D. Cremat, E. Jahner, L. Kim, C. Kundrak, H. Rajana, R. Riveros, and C. Simone for assistance with data collection and processing; P. Baniqued, A. Ghaderi, M. Polikoff, M. Lai, A. Montoya for advice on analyses; Artesia High School in Lakewood, CA, Rowland Unified School District in Rowland Heights, CA, and other participating public high schools for help with participant recruitment; A. Blodget, A. Damasio, H. Damasio, D. Daniel, R. Davidson, H. Gardner, A. Ghaderi, D. Knecht, and C.D. Lee for comments on an earlier version of the manuscript. This work was funded by grants from the National Science Foundation (CAREER 11519520; BCS 1522986) and Raikes Foundation (61405837-118286) and by gifts from ECMC Foundation and Stuart Foundation to MHIY; NSF GRFP and USC Provost’s Research Fellowship to RG.

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What is artificial general intelligence (AGI)?

You’ve read the think pieces. AI—in particular, the generative AI (gen AI) breakthroughs achieved in the past year or so—is poised to revolutionize not just the way we create content but the very makeup of our economies and societies as a whole. But although gen AI tools such as ChatGPT may seem like a great leap forward, in reality they are just a step in the direction of an even greater breakthrough: artificial general intelligence, or AGI.

Get to know and directly engage with senior McKinsey experts on AGI

Aamer Baig is a senior partner in McKinsey’s Chicago office; Federico Berruti is a partner in the Toronto office; Ben Ellencweig is a senior partner in the Stamford, Connecticut, office; Damian Lewandowski is a consultant in the Miami office; Roger Roberts is a partner in the Bay Area office, where Lareina Yee is a senior partner;  Alex Singla  is a senior partner in the Chicago office and the global leader of QuantumBlack, AI by McKinsey;  Kate Smaje  and Alex Sukharevsky  are senior partners in the London office;   Jonathan Tilley is a partner in the Southern California office; and Rodney Zemmel is a senior partner in the New York office.

AGI is AI with capabilities that rival those of a human . While purely theoretical at this stage, someday AGI may replicate human-like cognitive abilities including reasoning, problem solving, perception, learning, and language comprehension. When AI’s abilities are indistinguishable from those of a human, it will have passed what is known as the Turing test , first proposed by 20th-century computer scientist Alan Turing.

But let’s not get ahead of ourselves. AI has made significant strides in recent years, but no AI tool to date has passed the Turing test. We’re still far from reaching a point where AI tools can understand, communicate, and act with the same nuance and sensitivity of a human—and, critically, understand the meaning behind it. Most researchers and academics believe we are decades away from realizing AGI; a few even predict we won’t see AGI this century (or ever). Rodney Brooks, a roboticist at the Massachusetts Institute of Technology and cofounder of iRobot, believes AGI won’t arrive until the year 2300 .

If you’re thinking that AI already seems pretty smart, that’s understandable. We’ve seen gen AI  do remarkable things in recent years, from writing code to composing sonnets in seconds. But there’s a critical difference between AI and AGI. Although the latest gen AI technologies, including ChatGPT, DALL-E, and others, have been hogging headlines, they are essentially prediction machines—albeit very good ones. In other words, they can predict, with a high degree of accuracy, the answer to a specific prompt because they’ve been trained on huge amounts of data. This is impressive, but it’s not at a human level of performance in terms of creativity, logical reasoning, sensory perception, and other capabilities . By contrast, AGI tools could feature cognitive and emotional abilities (like empathy) indistinguishable from those of a human. Depending on your definition of AGI, they might even be capable of consciously grasping the meaning behind what they’re doing.

The timing of AGI’s emergence is uncertain. But when it does arrive—and it likely will at some point—it’s going to be a very big deal for every aspect of our lives, businesses, and societies. Executives can begin working now to better understand the path to machines achieving human-level intelligence and making the transition to a more automated world.

Learn more about QuantumBlack, AI by McKinsey .

What is needed for AI to become AGI?

Here are eight capabilities AI needs to master before achieving AGI. Click each card to learn more.

How will people access AGI tools?

Today, most people engage with AI in the same ways they’ve accessed digital power for years: via 2D screens such as laptops, smartphones, and TVs. The future will probably look a lot different. Some of the brightest minds (and biggest budgets) in tech are devoting themselves to figuring out how we’ll access AI (and possibly AGI) in the future. One example you’re likely familiar with is augmented reality and virtual reality headsets , through which users experience an immersive virtual world . Another example would be humans accessing the AI world through implanted neurons in the brain. This might sound like something out of a sci-fi novel, but it’s not. In January 2024, Neuralink implanted a chip in a human brain, with the goal of allowing the human to control a phone or computer purely by thought.

A final mode of interaction with AI seems ripped from sci-fi as well: robots. These can take the form of mechanized limbs connected to humans or machine bases or even programmed humanoid robots.

What is a robot and what types of robots are there?

The simplest definition of a robot is a machine that can perform tasks on its own or with minimal assistance from humans. The most sophisticated robots can also interact with their surroundings.

Programmable robots have been operational since the 1950s. McKinsey estimates that 3.5 million robots are currently in use, with 550,000 more deployed every year. But while programmable robots are more commonplace than ever in the workforce, they have a long way to go before they outnumber their human counterparts. The Republic of Korea, home to the world’s highest density of robots, still employs 100 times as many humans as robots.

Introducing McKinsey Explainers : Direct answers to complex questions

But as hardware and software limitations become increasingly surmountable, companies that manufacture robots are beginning to program units with new AI tools and techniques. These dramatically improve robots’ ability to perform tasks typically handled by humans, including walking, sensing, communicating, and manipulating objects. In May 2023, Sanctuary AI, for example, launched Phoenix, a bipedal humanoid robot that stands 5’ 7” tall, lifts objects weighing as much as 55 pounds, and travels three miles per hour—not to mention it also folds clothes, stocks shelves, and works a register.

As we edge closer to AGI, we can expect increasingly sophisticated AI tools and techniques to be programmed into robots of all kinds. Here are a few categories of robots that are currently operational:

• Stand-alone autonomous industrial robots : Equipped with sensors and computer systems to navigate their surroundings and interact with other machines, these robots are critical components of the modern automated manufacturing industry.
• Collaborative robots : Also known as cobots, these robots are specifically engineered to operate in collaboration with humans in a shared environment. Their primary purpose is to alleviate repetitive or hazardous tasks. These types of robots are already being used in environments such as restaurant kitchens and more.
• Mobile robots : Utilizing wheels as their primary means of movement, mobile robots are commonly used for materials handling in warehouses and factories. The military also uses these machines for various purposes, such as reconnaissance and bomb disposal.
• Human–hybrid robots : These robots have both human and robotic features. This could include a robot with an appearance, movement capabilities, or cognition that resemble those of a human, or a human with a robotic limb or even a brain implant.
• Humanoids or androids : These robots are designed to emulate the appearance, movement, communicative abilities, and emotions of humans while continuously enhancing their cognitive capabilities via deep learning models. In other words, humanoid robots will think like a human, move like a human, and look like a human.

What advances could speed up the development of AGI?

Advances in algorithms, computing, and data  have brought about the recent acceleration of AI. We can get a sense of what the future may hold by looking at these three capabilities:

Algorithmic advances and new robotics approaches . We may need entirely new approaches to algorithms and robots to achieve AGI. One way researchers are thinking about this is by exploring the concept of embodied cognition. The idea is that robots will need to learn very quickly from their environments through a multitude of senses, just like humans do when they’re very young. Similarly, to develop cognition in the same way humans do, robots will need to experience the physical world like we do (because we’ve designed our spaces based on how our bodies and minds work).

The latest AI-based robot systems are using gen AI technologies including large language models (LLMs) and large behavior models (LBMs). LLMs give robots advanced natural-language-processing capabilities like what we’ve seen with generative AI models and other LLM-enabled tools. LBMs allow robots to emulate human actions and movements. These models are created by training AI on large data sets of observed human actions and movements. Ultimately, these models could allow robots to perform a wide range of activities with limited task-specific training.

A real advance would be to develop new AI systems that start out with a certain level of built-in knowledge, just like a baby fawn knows how to stand and feed without being taught. It’s possible that the recent success of deep-learning-based AI systems may have drawn research attention away from the more fundamental cognitive work required to make progress toward AGI.

• Computing advancements. Graphics processing units (GPUs) have made the major AI advances of the past few years possible . Here’s why. For one, GPUs are designed to handle multiple tasks related to visual data simultaneously, including rendering images, videos, and graphics-related computations. Their efficiency at handling massive amounts of visual data makes them useful in training complex neural networks. They also have a high memory bandwidth, meaning faster data transfer. Before AGI can be achieved, similar significant advancements will need to be made in computing infrastructure. Quantum computing  is touted as one way of achieving this. However, today’s quantum computers, while powerful, aren’t yet ready for everyday applications. But once they are, they could play a role in the achievement of AGI.

Growth in data volume and new sources of data . Some experts believe 5G  mobile infrastructure could bring about a significant increase in data. That’s because the technology could power a surge in connected devices, or the Internet of Things . But, for a variety of reasons, we think most of the benefits of 5G have already appeared . For AGI to be achieved, there will need to be another catalyst for a huge increase in data volume.

New robotics approaches could yield new sources of training data. Placing human-like robots among us could allow companies to mine large sets of data that mimic our own senses to help the robots train themselves. Advanced self-driving cars are one example: data is being collected from cars that are already on the roads, so these vehicles are acting as a training set for future self-driving cars.

What can executives do about AGI?

AGI is still decades away, at the very least. But AI is here to stay—and it is advancing extremely quickly. Smart leaders can think about how to respond to the real progress that’s happening, as well as how to prepare for the automated future. Here are a few things to consider:

• Stay informed about developments in AI and AGI . Connect with start-ups and develop a framework for tracking progress in AGI that is relevant to your business. Also, start to think about the right governance, conditions, and boundaries for success within your business and communities.
• Invest in AI now . “The cost of doing nothing,” says McKinsey senior partner Nicolai Müller , “is just too high  because everybody has this at the top of their agenda. I think it’s the one topic that every management board  has looked into, that every CEO  has explored across all regions and industries.” The organizations that get it right now will be poised to win in the coming era.
• Continue to place humans at the center . Invest in human–machine interfaces, or “human in the loop” technologies that augment human intelligence. People at all levels of an organization need training and support to thrive in an increasingly automated world. AI is just the latest tool to help individuals and companies alike boost their efficiency.
• Consider the ethical and security implications . This should include addressing cybersecurity , data privacy, and algorithm bias.
• Build a strong foundation of data, talent, and capabilities . AI runs on data; having a strong foundation of high-quality data is critical to its success.
• Organize your workers for new economies of scale and skill . Yesterday’s rigid organizational structures and operating models aren’t suited to the reality of rapidly advancing AI. One way to address this is by instituting flow-to-the-work models, where people can move seamlessly between initiatives and groups.
• Place small bets to preserve strategic options in areas of your business that are exposed to AI developments . For example, consider investing in technology firms that are pursuing ambitious AI research and development projects in your industry. Not all these bets will necessarily pay off, but they could help hedge some of the existential risk your business may face in the future.

Learn more about QuantumBlack, AI by McKinsey . And check out AI-related job opportunities if you’re interested in working at McKinsey.

Articles referenced:

• “ Generative AI in operations: Capturing the value ,” January 3, 2024, Marie El Hoyek and  Nicolai Müller
• “ The economic potential of generative AI: The next productivity frontier ,” June 14, 2023, Michael Chui , Eric Hazan , Roger Roberts , Alex Singla , Kate Smaje , Alex Sukharevsky , Lareina Yee , and Rodney Zemmel
• “ What every CEO should know about generative AI ,” May 12, 2023, Michael Chui , Roger Roberts , Tanya Rodchenko, Alex Singla , Alex Sukharevsky , Lareina Yee , and Delphine Zurkiya
• “ An executive primer on artificial general intelligence ,” April 29, 2020, Federico Berruti , Pieter Nel, and Rob Whiteman
• “ Notes from the AI frontier: Applications and value of deep learning ,” April 17, 2018, Michael Chui , James Manyika , Mehdi Miremadi, Nicolaus Henke, Rita Chung, Pieter Nel, and Sankalp Malhotra
• “ Augmented and virtual reality: The promise and peril of immersive technologies ,” October 3, 2017, Stefan Hall and Ryo Takahashi

Want to know more about artificial general intelligence (AGI)?

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