An Old Mind, a New Health System: An Introduction to Evolutionary Psychology for Health Management

Abstract

This report, written under the title “An Old Mind, a New Health System”, examines in two parts how evolutionary psychology can be applied to health management. Its central claim is that the most persistent problems in health management (poor hand hygiene compliance, vaccine hesitancy, burnout and violations of safety protocols) cannot be explained by “lack of knowledge” or “insufficient incentives”. People behave this way not because they are ignorant, but because they respond predictably in particular contexts.

Part One builds the conceptual framework. It introduces the idea that the mind is not a general-purpose computer but a set of domain-specific systems, the distinction between proximate and ultimate causation, and evolutionary mismatch (ancestrally adaptive tendencies producing harm in modern environments) as the most productive bridging concept. It then outlines seven functional areas of intersection: patient safety and infection control (the behavioural immune system and disgust), human resources (burnout, status and mobbing), health policy (the deservingness heuristic and resource allocation), health behaviour (life history strategy), clinical services, facility design (biophilia) and digital health.

Part Two deepens these seven areas with a fixed three-part structure: the current state of the evidence, managerial implications, and numbered, testable research questions for the Turkish health system (RQ7.1 to RQ13.4). Evidence maturity is separated honestly: disease avoidance, status threat and the deservingness heuristic are relatively established; life history and contact with nature are developing; and the evolutionary extension of technology acceptance is still at the level of theoretical proposal.

Three ethical and methodological cautions are maintained throughout: avoiding the naturalistic fallacy (a tendency being evolved does not make it legitimate), treating evolutionary explanation as a complement to, not a substitute for, institutional, cultural and economic explanations, and valuing the framework only when it produces a new, falsifiable prediction. The report closes with a common methodological framework (Table 3) and argues that Türkiye, with its extensive administrative data infrastructure, family medicine records and national surveys, is a favourable setting for these tests, and that the real opportunity is not to replicate international findings but to generate new questions from the specificity of the local structure.

Part One: Conceptual Framework

1. Introduction: Questions Where Managerial Tools Fall Short

Over the past thirty years health management has reached considerable maturity in evidence-based policy making, process improvement and performance measurement. Yet some of the field’s most fundamental problems continue to show surprising resistance to technical solutions and regulatory interventions. Hand hygiene compliance fails to reach target levels in most institutions, even though its effectiveness is beyond dispute and heavy investment has been made in training. Safety protocols are violated even when practitioners do not lack knowledge and are aware of the risk. Vaccine hesitancy can increase in inverse proportion to the accessibility of scientific information. Burnout recedes only partially with workload adjustments and additional staffing.

These patterns share a common feature: explaining the behaviour by “lack of knowledge” or “insufficient incentives” is empirically inadequate. The behavioural theories most commonly used in health management (the theory of planned behaviour, the health belief model, technology acceptance models) are strong at describing the components through which behaviour occurs. However, they leave open the question of why people respond to certain inputs so systematically, with such cross-cultural consistency and often in spite of their own stated intentions. This gap is not trivial from a managerial point of view: an intervention design that does not know why a behaviour is so persistent is inevitably condemned to trial and error.

Evolutionary psychology proposes a level of explanation aimed at filling precisely this gap. In the public administration literature it has been argued explicitly that this perspective offers a fresh lens for understanding and changing problematic behaviour (Tybur & Griskevicius, 2013). The aim of this text is to introduce evolutionary psychology to health management readers who have never encountered it, to present its core concepts in simplified form, and to show how these concepts intersect with managerial functions ranging from patient safety and human resource management to health policy and facility design.

2. What Is Evolutionary Psychology? The Core Conceptual Framework

2.1. The mind is not a general-purpose computer

Evolutionary psychology is an explanatory framework that treats the human mind as a collection of relatively specialised information-processing mechanisms, shaped by natural selection to solve recurring problems of survival and reproduction in the environments our ancestors lived in. The critical claim is that the mind is not a general-purpose device that processes all inputs equally. Instead, there are domain-specific systems that respond preferentially and rapidly to particular classes of input: cues of contamination, status threats, signals of exclusion and cues of resource scarcity (Neuberg et al., 2011).

This approach has a direct managerial consequence: some behaviours encountered in healthcare organisations may stem not from people’s foolishness or indifference, but from response systems that are triggered automatically in particular contexts. In that case the target of intervention is not to increase knowledge but to redesign the contextual cues that trigger the relevant system.

2.2. The distinction between proximate and ultimate causes

Probably the single most useful conceptual tool evolutionary psychology offers health management is the distinction between proximate and ultimate causation. The proximate cause explains how a behaviour arises in the moment: which cognitive process, which hormone, which immediate stimulus. The ultimate cause explains why that mechanism exists at all, that is, which recurring adaptive problem it solves. The two levels are not alternatives; they are complementary.

The applied value of the distinction became clear during COVID-19. When compliance or non-compliance with social distancing rules was examined by analysing both the immediate psychological determinants of the behaviour and the evolutionary function of norm-following together, patterns that single-level explanations missed became visible (Norton et al., 2021). Similarly, modelling non-compliance with safety rules not as simple negligence but as a free-riding strategy that emerges in cooperative settings explained why the design of monitoring and transparency determines compliance (Yong & Choy, 2021).

2.3. Evolutionary mismatch: the bridging concept

For health management the most productive concept is without doubt evolutionary mismatch. The concept states that tendencies that supported survival in our ancestral environment can produce systematic harm in a modern environment that differs radically from it. Gravitating towards high-calorie food, preferring short-term over long-term rewards, the capacity to stay awake at night, wariness of strangers: none of these are pathological tendencies; only the context in which they occur has changed.

This framework has been proposed as an explicit programme for solving modern problems (Li et al., 2020). How food preferences produce mismatched outcomes in modern marketplaces (Folwarczny et al., 2023), how evolved propensities for nocturnal activity complicate epidemic control (Varella et al., 2021), and how medical interventions in childbirth may interrupt an adaptive birthing consciousness (Dahan, 2021) are concrete applications of this approach. The message for the health manager is clear: some “unhealthy behaviours” are not individual failings but a predictable output of the mismatch between environment and evolved tendency, and can therefore be addressed through environmental design.

3. Where Evolutionary Psychology Meets Health Management Functions

The table below matches the core concepts discussed in this text with health management functions. The subsections that follow elaborate on each intersection.

Table 1. Evolutionary psychology concepts and their counterparts in health management

Evolutionary concept	Core proposition	Health management counterpart
Behavioural immune system	Disgust is an avoidance system that prevents pathogen contact at the behavioural level.	Hand hygiene, isolation compliance, design of infection control
Evolutionary mismatch	Tendencies that were functional in ancestral environments can produce harm in modern ones.	Obesity, physical inactivity, shift work and sleep patterns, digital fatigue
Life history strategy	Unpredictable, resource-constrained environments strengthen short-term orientation.	Demand for preventive services, treatment adherence, health inequalities
Status and social belonging	Loss of status and exclusion trigger strong physiological stress responses.	Burnout, mobbing, physician-nurse hierarchy, safety climate
Cooperation and free-riding	Cooperation is stable only when contributions are visible and can be monitored.	Protocol compliance, teamwork, fairness of on-call and shift allocation
Deservingness heuristic	Decisions to help are made rapidly according to whether the person is responsible for their situation.	Resource allocation, prioritisation, public support, stigma
Biophilia	The human mind responds to cues of the natural environment in a stress-reducing way.	Hospital design, healing environments, waiting areas

3.1. Patient safety and infection control

The human behavioural immune system is conceptualised as an avoidance mechanism that is activated before contact with a pathogen. It has been shown in detail that disgust is an adapted system that prevents disease transmission at the behavioural level (Curtis et al., 2011). The consequence for infection control is direct: hand hygiene behaviour is triggered more by concrete cues of contamination than by abstract risk information. Indeed, designing behaviour change interventions around evolutionary and emotional triggers has been systematised as an applied science of behaviour change (Aunger & Curtis, 2016).

The same system also has outputs that are undesirable from a managerial perspective. Contamination aversion has been shown to predict more negative vaccine attitudes (Clay, 2017); in other words, the tendency to avoid disease can also operate against a protective medical intervention. Similarly, how stigma is amplified by cultural patterns during epidemics has been documented systematically (Zay Hta et al., 2024). For the health manager this means that the same psychological system can both support hand hygiene and fuel vaccine refusal and the stigmatisation of patients; intervention design has to take this duality into account.

3.2. Human resource management: burnout, hierarchy and mobbing

Healthcare organisations are work environments with intense hierarchy and continuous status evaluation. The evolutionary view reframes burnout not only as a function of workload but as a response pattern that arises in the face of loss of social rank and threats to belonging (Garcia, 2024). This framing has the power to explain why burnout declines when recognition and team belonging increase while workload stays the same. The adaptationist analysis showing that the acute stress response cannot be reduced to a fight-or-flight dichotomy and forms a spectrum that includes freezing and fainting (Bracha, 2004) shows why performance judgements must be made carefully when evaluating how staff in high-acuity units respond to critical incidents.

A set of related findings completes the picture. The outcomes of excessively competitive productivity norms, extending as far as fatal consequences, have been explained with a multilevel evolutionary model (Timming, 2021). Certain personality traits have been shown to play a protective role in individuals facing workplace bullying (Dåderman & Basinska, 2021). The potential effects of organising the work environment according to evolutionary principles on employee health, happiness and productivity have also been discussed (Fitzgerald & Danner, 2012). Findings that perceptions of leadership are systematically influenced by physical cues (Blaker et al., 2013) and that appearance is associated with occupational success (Little & Roberts, 2012) suggest that implicit biases in promotion and recruitment may have deeper perceptual roots, not only cultural ones. The framework on the psychological and social costs of inequality (Wilkinson & Pickett, 2017) provides a complementary basis for understanding how status differences within an organisation are reflected in employee health.

3.3. Health policy, resource allocation and public support

The hardest decisions in health policy concern to whom scarce resources should be allocated. It has been shown that these decisions cannot be explained by ideological position alone, and that people base their decisions to help on a rapid and largely automatic assessment of “deservingness”. The intuition about whether a person is responsible for their own situation is a strong predictor of welfare attitudes (Petersen et al., 2012), and this intuition has been shown empirically to shape health policy preferences directly (Jensen & Petersen, 2017).

This finding carries a practical warning for health managers and policy makers: public resistance to services related to smoking, obesity or addiction often stems not from cost calculations but from the activation of the deservingness heuristic. When the communication frame of a policy does not take this intuition into account, even technically correct arguments may fail to generate public support.

3.4. Health behaviour, demand and preventive services

Life history theory explains how individuals allocate their resources between fast and slow strategies. Within this framework, the orientation towards short-term rewards of individuals who grow up in unpredictable, resource-constrained and early-stress environments is seen as an adapted response. The relationship of this strategic orientation with food choice and calorie intake has been shown experimentally (Laran & Salerno, 2013), and its link with obesity has been demonstrated with large-scale data (Maner et al., 2017). Findings that early-life conditions permanently calibrate an individual’s sensitivity to environmental influences complete the picture (Ellis & Boyce, 2008).

The framework has two consequences for health management. First, low participation in preventive health services cannot be read solely as a problem of access or awareness; any intervention that requires accepting a cost today for a future gain will systematically produce lower responses in groups following a fast life history strategy. Second, the message frame of health communication can be designed to be sensitive to this difference; indeed, it has been shown that the effect of lethal versus reproductive messages in preventive health advertising varies according to life history strategy (Ivanov et al., 2021). Treating addictive behaviour through a biopsychosocial and evolutionary synthesis (Lende & Smith, 2002) offers a similar reframing.

3.5. Clinical service delivery and patient-professional interaction

The evolutionary perspective also extends to the everyday details of clinical interaction. It has been argued that the facial expression of pain is an evolved signal with a communicative function (Williams, 2002), and that the reporting of pain intensity and observers’ coding of it vary systematically by biological sex and social setting (Vigil & Coulombe, 2011). These findings show that pain assessment protocols and triage decisions are structurally open to observer-dependent bias; this is directly a matter of patient safety and service quality.

Similarly, grounding the placebo effect in an evolutionary logic related to the organism’s resource allocation decisions (Trimmer et al., 2013) helps explain why the treatment context and the expectation of recovery produce measurable clinical effects. The evolutionary roots of psychological healing and their meaning for global mental health services have also been addressed (Kohrt et al., 2020).

For mental health services, the discussion of the paradox of common, heritable disorders through evolutionary genetic models (Keller & Miller, 2006), the linking of evolutionary theory with psychiatry and psychopharmacology (Stein, 2006), the joint evaluation of the functional and dysfunctional aspects of social anxiety (Maner & Kenrick, 2010) and the placement of bipolar disorder within a psychoneuroimmunological framework (Rantala et al., 2021) illustrate the theoretical expansion in this area. The evolutionary foundations of compassion focused therapy (Gilbert, 2014), the clinical significance of loneliness (Heinrich & Gullone, 2006), treatment of suicide as an evolutionary puzzle (Aubin et al., 2013) and the reappraisal of the relationship between maternal age and postpartum depression through evolutionary theory (Bottino et al., 2012) produce concrete implications for service planning.

3.6. Physical environment, facility design and digital health

The biophilia hypothesis, which holds that the human mind responds to natural environmental cues in a stress-reducing way, has been evaluated in the context of the effect of views of nature on health and well-being (Grinde & Patil, 2009), and the benefits of nature experiences have been comprehensively reviewed (Franco et al., 2017). These findings are directly applicable to hospital design and the planning of waiting areas and staff rest spaces.

In digital health, it has been argued explicitly that evolutionary psychology can enrich technology acceptance theories (Abraham et al., 2013). Given that users’ responses to new technologies relate not only to perceived usefulness and ease of use but also to deeper systems such as trust, status and threat appraisal, this extension holds promise for explaining the adoption of telehealth and wearable monitoring systems (Drury, 2014). The comprehensive proposal that intentional change at the institutional level can be grounded in evolutionary principles (Wilson et al., 2014) provides the common theoretical framework for these applications. Findings on how the intergenerational distribution of the care burden affects health and well-being (Danielsbacka et al., 2019) provide a complementary basis for informal care policies.

4. Limits, Criticisms and Ethical Cautions

The transfer of evolutionary psychology to health management must be made with important caveats. The first and most critical is avoiding the naturalistic fallacy: the evolutionary origin of a tendency does not make it legitimate, inevitable or desirable. The existence of an evolutionary explanation for hierarchical domination, mobbing or stigma does not mean these behaviours are excused; on the contrary, knowing the contextual conditions under which they emerge makes it possible to design more accurate measures to prevent them.

Second, evolutionary explanations have often been criticised for producing retrospective narratives that are hard to verify. This criticism is justified and has pushed the field towards building predictive, testable hypotheses. For the health management researcher the practical criterion is clear: an evolutionary framework has value only when it provides a new and testable prediction that existing theories cannot produce. Third, evolutionary explanations must not slide into biological reductionism; institutional structure, culture, professional norms and economic incentives remain indispensable components of explanation. The function of the evolutionary perspective is not to replace these variables, but to make sense of why they produce effects in particular directions and with particular strength.

5. Conclusion and the Position of This Study

Health management is a discipline that manages people, yet its implicit assumptions about how the human mind it manages is designed often remain untested. Evolutionary psychology offers a framework that makes these assumptions explicit and adds a new level of explanation to the field’s persistent problems: non-compliance, burnout, free-riding, stigma and low participation in preventive services. The promise of this framework is not to replace existing managerial theories, but to give the patterns they struggle to explain a grounding at the level of ultimate causation.

The following sections take up the areas of intersection outlined above one by one; for each area the current state of evidence, managerial implications and testable research questions in the context of the Turkish health system are discussed. The aim is not to carry evolutionary psychology into health management as an exotic borrowed concept, but to turn it into a functional tool that enables the field to ask its own questions more deeply.

Part Two: Areas of Intersection, State of Evidence and a Research Agenda for Türkiye

6. Aim and Structure of This Part

Part One introduced the core conceptual framework of evolutionary psychology and argued that it intersects with seven functional areas of health management. This part fulfils the promise made there and treats each area of intersection under three fixed headings: the current state of evidence, managerial implications, and testable research questions in the context of the Turkish health system.

There is a reason for insisting on this three-part structure. The most common failure of interdisciplinary transfers is that a theoretical framework is found interesting and adopted but never tested empirically, and is therefore abandoned within a few years. A framework gains permanence in health management only if it produces predictions that can be tested with local data. For this reason each section closes its conceptual discussion with numbered, operationalisable research questions.

A uniform language is used in assessing the state of evidence. The difference in maturity between areas is real, and hiding it would damage the credibility of the framework. Table 2 summarises this distinction.

Table 2. Classification of the areas of intersection by evidence maturity

Evidence maturity	Definition	Areas in this text
Relatively established	Multiple independent samples, cross-cultural replication, experimental support	Disease avoidance and disgust; status and belonging threat; deservingness heuristic
Developing	Consistent but limited number of studies; measurement standard not yet settled	Life history strategy and health behaviour; contact with nature and recovery
Theoretical proposal	Primarily conceptual; little or no direct testing	Evolutionary psychiatric classification; evolutionary extension of technology acceptance

7. Patient Safety and Infection Control

7.1. State of evidence

This area has the most mature evidence base that evolutionary psychology offers health management. It has been shown in detail that disgust is an adapted disease-avoidance system that is activated before contact with a pathogen occurs (Curtis et al., 2011), and that this system is part of a wider threat management architecture (Neuberg et al., 2011). The distinguishing feature of the system is that it is triggered not by abstract risk information but by concrete sensory cues; visible dirt, smell and texture produce a much stronger behavioural effect than a calculated probability of infection.

The translation of this finding into practice has been systematised as a behaviour change design methodology that places evolutionary and emotional triggers at the centre (Aunger & Curtis, 2016). The COVID-19 period made the limits of the system visible. Changes in disgust sensitivity among women during the outbreak were documented (Milkowska et al., 2021), social distancing behaviour was shown to be a trade-off between affiliation and disease-avoidance motivations (Young et al., 2021), and norm-following was shown to require analysis at both proximate and ultimate levels (Norton et al., 2021). It has also been shown that non-compliance with rules can be modelled not as simple negligence but as a free-riding strategy in cooperative settings (Yong & Choy, 2021).

The undesirable outputs of the same system are also well documented. Contamination aversion can predict negative vaccine attitudes (Clay, 2017); perceived disease protection changes attitudes towards out-groups (Huang et al., 2011); not wearing a mask can, in some contexts, function more as a coalitional signal of belonging than as a health behaviour (Boykin et al., 2021); and epidemic narratives are reshaped by local cultural frames (Goodwin et al., 2011). The cultural determinants of stigma during epidemics have been reviewed systematically (Zay Hta et al., 2024).

7.2. Managerial implications

The first implication is to shift hand hygiene interventions from information transfer to cue design. Posters reporting infection rates may not be as effective as arrangements containing concrete contamination cues that trigger the behavioural immune system. The location and visibility of sanitiser stations and the sensory feedback at the moment of contact may be more decisive than hours of training; this is a directly testable managerial proposition.

The second implication is the sensitivity of compliance behaviour to social visibility. The free-riding model predicts that cooperation increases in settings where contributions are observable. This means that hand hygiene observation programmes are an intervention tool as much as a measurement tool; however, the same mechanism also predicts that compliance will fall when observation is removed. The practical question for the manager is how to make compliance visible without making it dependent on external surveillance.

The third implication concerns stigma. The disease-avoidance system also produces avoidance responses towards infected patients and those who care for them. The social exclusion of healthcare workers during epidemics is a predictable consequence of this mechanism, and institutional communication planning should take this risk into account in advance.

7.3. Testable research questions for Türkiye

• RQ7.1. Does disgust sensitivity significantly predict hand hygiene compliance (measured by direct observation) in Turkish hospitals when professional seniority and education are controlled for? Does its predictive power differ between physicians, nurses and support staff?
• RQ7.2. In an in-hospital randomised trial comparing visual arrangements with concrete contamination cues at sanitiser stations with arrangements presenting statistical risk information, is there a difference in compliance rates, and how long does this difference last after observation is removed?
• RQ7.3. Does hand hygiene compliance in intensive care and ward units increase when the visibility of contributions within the team increases? As the free-riding model predicts, is compliance inversely related to team size?
• RQ7.4. In a Turkish sample, is vaccine hesitancy positively or negatively related to contamination aversion? Is this relationship mediated by the perception of the vaccine as an unnatural substance?
• RQ7.5. During epidemics, is the severity of social avoidance behaviour towards healthcare workers related to the perceived contagiousness of the patient group the worker is in contact with, and can this relationship be weakened by institutional communication interventions?

8. Human Resource Management: Burnout, Hierarchy and Workplace Violence

8.1. State of evidence

Healthcare organisations are work environments with intense hierarchy, continuous evaluation and high visibility. The evolutionary perspective reframes burnout not as a linear function of workload but as a response pattern to loss of social rank and threats to belonging (Garcia, 2024). This framing can explain why burnout declines when recognition and team belonging increase while workload stays constant.

The details of the status system have also been documented. Shame has been shown to track the threat of devaluation by others with cross-cultural consistency (Sznycer et al., 2016), and anger has been shown to have a computational architecture that serves to recalibrate bargaining power (Sell et al., 2009, 2017). The model in which perceptions of safety and threat in intergroup relations operate through a coalitional index (Boyer et al., 2015) is directly useful for understanding tensions between professional groups. It has also been shown that coping behaviour can be decomposed into dimensions of evolutionary origin (Gutiérrez et al., 2007).

At the organisational level, the outcomes of excessively competitive productivity norms, reaching fatal consequences, have been explained with a multilevel evolutionary model (Timming, 2021); certain personality traits have been shown to play a protective role in individuals facing workplace bullying (Dåderman & Basinska, 2021); and the potential of organising the work environment according to evolutionary principles for employee health and productivity has been discussed (Fitzgerald & Danner, 2012). Findings that leadership perceptions are systematically influenced by physical and developmental cues (Blaker et al., 2013; Senior et al., 2012), that leadership hierarchies form rapidly through bodily signals even in leaderless groups (Campbell et al., 2002), and that appearance is associated with occupational success (Little & Roberts, 2012) point to the perceptual roots of implicit bias in promotion and recruitment. The framework on the psychological and social costs of inequality (Wilkinson & Pickett, 2017) completes this picture.

8.2. Managerial implications

The first implication is to broaden the target variable of burnout interventions. Programmes that focus only on workload and working hours will have limited effect to the extent that they neglect the dimensions of status and belonging. In contrast, making contributions visible, involvement in decision processes and perceived fairness within the team are directly targetable intervention areas.

The second implication concerns the design of pay-for-performance systems. Given the strength of sensitivity to status and fairness, the effect these systems produce is expected to be related more to changes in relative position than to the absolute amount earned. The same additional payment can reduce motivation when it lowers relative ranking; this is a directly testable prediction.

The third implication concerns the management of workplace violence and bullying. The model holding that anger serves to recalibrate bargaining power predicts that aggressive behaviour often follows a perception of being devalued. This means that violence in healthcare can be addressed not only through security measures but also through service design that increases the perception among patients and relatives that they are treated with respect during the process.

The fourth implication concerns selection and promotion processes. Findings that leadership perceptions are influenced by bodily and appearance-based cues show that unstructured interviews are structurally open to implicit bias; structured, criterion-based assessment tools can therefore be defended not only as a legal but also as a psychological necessity.

8.3. Testable research questions for Türkiye

• RQ8.1. Among healthcare workers, is burnout additionally explained by status threat and perceived belonging after workload indicators are controlled for? Does this additional explanatory power differ between physicians and nurses?
• RQ8.2. In the performance-based supplementary payment system, which predicts turnover intention more strongly: the change in the absolute amount of a worker’s supplementary payment, or the change in their relative ranking within the unit?
• RQ8.3. Is nurses’ intention to emigrate related, beyond economic expectations, to the perceived social status of the profession and experiences of devaluation? Is this relationship weakened by institutional recognition mechanisms?
• RQ8.4. Is there a systematic pattern in the waiting time, level of information and perceived respect of patients or relatives preceding incidents of violence in healthcare? Are “white code” records suitable for testing this pattern?
• RQ8.5. In a manager selection process comparing structured and unstructured interviews, is the effect of variables related to the candidate’s physical appearance on assessment scores significantly reduced?
• RQ8.6. Does perceived fairness within the team (measured through the allocation of on-call duties and shifts) predict burnout independently of workload?

9. Health Policy, Resource Allocation and Stigma

9.1. State of evidence

The hardest decisions in health policy concern to whom scarce resources are allocated. It has been shown that public reactions to these decisions cannot be explained by ideological position alone. People base their decisions to help on a rapid and largely automatic assessment of deservingness, that is, whether the person is responsible for their own situation (Petersen et al., 2012). This intuition has been shown empirically to shape health policy preferences directly (Jensen & Petersen, 2017).

A second line of evidence concerns stigma. Mental illness has been shown to function as a dealbreaker in relationship decisions (Boysen et al., 2019), and why public perception of depression as a disorder varies has been discussed in an evolutionary framework (Blease, 2012). The cultural determinants of stigma during epidemics have been reviewed systematically (Zay Hta et al., 2024), and the individual, intergroup and national-level determinants of conspiracy beliefs have been mapped comprehensively (Hornsey et al., 2023). The coalitional index model of intergroup threat (Boyer et al., 2015) offers a usable framework for analysing attitudes towards migrant and minority groups in access to health services.

9.2. Managerial implications

The first implication concerns the framing of policy communication. Public resistance to services related to smoking, obesity, addiction and similar behavioural risks often stems not from cost calculations but from the activation of the deservingness heuristic. Technically correct cost-effectiveness arguments may fail to generate public support unless they take this intuition into account. In contrast, frames that reduce attributions of responsibility (inherited predisposition, environmental exposure, early-life conditions) may increase support; this is a proposition that can be tested directly with vignette experiments.

The second implication concerns the design of anti-stigma programmes. To the extent that stigma stems from avoidance and exclusion systems rather than lack of knowledge, information-based campaigns will have limited effect. Contact-based interventions and an emphasis on shared belonging are more consistent with the predictions of the coalitional index model.

The third implication is the prioritisation of institutional communication during epidemics and crises. Given the multilevel determinants of conspiracy beliefs, trust building is related not only to the flow of information but to the institution being perceived as a trustworthy coalition member.

9.3. Testable research questions for Türkiye

• RQ9.1. In a Turkish sample, does support for public coverage of the same health service (for example, bariatric surgery or addiction treatment) vary systematically according to the degree to which the patient is held responsible for their condition? Does this effect persist when income and political orientation are controlled for?
• RQ9.2. When information frames that reduce attributions of responsibility (genetic predisposition, early-life conditions) are compared with cost-effectiveness frames, which increases support for public financing more strongly?
• RQ9.3. In which patient groups is stigma towards mental health services highest in Türkiye, and is this pattern more strongly related to perceived contagiousness or to perceived unpredictability?
• RQ9.4. As the coalitional index model predicts, do public attitudes towards migrant groups using health services harden when perceived resource scarcity increases?
• RQ9.5. Is the prevalence of conspiracy beliefs about vaccination and treatment processes more strongly related to general trust in the institution, or to the perception that the institution represents one’s own group?

10. Health Behaviour, Demand for Preventive Services and Addiction

10.1. State of evidence

Life history theory explains how individuals allocate their resources between fast and slow strategies. Within this framework, the orientation towards short-term rewards of individuals who develop in unpredictable, resource-constrained and early-stress environments is seen not as pathology but as an adaptation sensitive to conditions. The relationship of strategic orientation with food choice and calorie intake has been shown experimentally (Laran & Salerno, 2013), and its link with obesity has been demonstrated with large-scale data (Maner et al., 2017). Findings that early-life conditions permanently calibrate sensitivity to environmental influences (Ellis & Boyce, 2008) and that the early family environment predicts reproductive timing (Ellis et al., 2003) complete this picture. It has also been suggested that expectations may mediate the link between life history strategy and psychopathology (Kavanagh & Kahl, 2018).

The mismatch framework is particularly productive in this area. The modern market environment has been shown to push evolved food preferences towards mismatched outcomes (Folwarczny et al., 2023; Otterbring, 2020), and re-evaluating eating interventions through an evolutionary lens has been proposed (Ahlstrom et al., 2017). Treating addictive behaviour through a biopsychosocial and evolutionary synthesis (Lende & Smith, 2002) and mapping the shared and distinct pathways of substance and behavioural addictions (Hunt et al., 2024) show the theoretical deepening in this area.

The finding that generalised reciprocity supports recovery in therapeutic communities (Doogan & Warren, 2017) is directly relevant to service design. It has also been shown that the effect of lethal versus reproductive messages in preventive health communication varies according to life history strategy (Ivanov et al., 2021).

10.2. Managerial implications

The first and most important implication is this: any intervention that requires accepting a cost today for a future gain (cancer screening, regular check-ups, chronic disease follow-up, lifestyle change) will systematically produce lower responses in groups following a fast life history strategy. This effect is independent of lack of awareness and cannot be closed simply by providing more information. The persistence of inequalities in preventive services can be partly explained by this mechanism.

The second implication is that this observation does not lead to a fatalistic conclusion. The fast strategy is sensitive to perceived unpredictability; arrangements that make access predictable (guaranteed appointments, continuity with the same physician, clarity of waiting times) can therefore increase participation by lowering the behavioural cost. This is a lever that health management can control directly.

The third implication is that message framing can be differentiated according to the target group. Findings that fear messages based on death do not work equally well in every group show that a uniform approach to campaign design is costly.

The fourth implication is that group-based reciprocity mechanisms in addiction services should be supported institutionally; findings that recovery progresses through social bonds suggest that individual treatment models alone may be insufficient.

10.3. Testable research questions for Türkiye

• RQ10.1. Do life history strategy indicators significantly predict participation in cancer screening programmes in Türkiye (KETEM services) after income and education are controlled for?
• RQ10.2. In the TurkStat Health Survey microdata, does the relationship between indicator variables of early-life conditions and the use of preventive services offer additional explanatory power beyond existing socioeconomic variables?
• RQ10.3. In the family medicine system, does physician continuity (the length of time a patient stays with the same physician) increase adherence to chronic disease follow-up more strongly in individuals following a fast life history strategy than in those following a slow one?
• RQ10.4. Do arrangements that increase appointment predictability reduce no-show rates disproportionately in low socioeconomic groups compared with high ones?
• RQ10.5. Does the effect of death-based versus quality-of-life-based messages in preventive health campaigns differ according to the life history orientation of the target audience?
• RQ10.6. In addiction treatment centres, does the level of within-group reciprocity predict treatment completion rates independently of individual variables?

11. Clinical Service Delivery and Decision Making

11.1. State of evidence

The evolutionary perspective also extends to the everyday details of clinical interaction. It has been argued that the facial expression of pain is an evolved signal with a communicative function (Williams, 2002), and it has been shown that pain reporting and observer coding vary systematically by biological sex and social setting (Vigil & Coulombe, 2011). These findings show that pain assessment, and therefore triage and analgesia decisions, are structurally open to observer-dependent bias.

Error management theory is particularly important for decision making. The theory predicts that when the costs of two types of error are asymmetric, it is adaptive for the system to be biased towards the cheaper error; it has been discussed that unrealistic optimism and similar biases can be reinterpreted within this framework (Jefferson, 2017). The clinical counterpart is direct: when the cost of a false negative is higher than that of a false positive, a clinician’s drift towards overdiagnosis is not irrationality but a predictable response to an asymmetric cost structure.

As regards the treatment context, grounding the placebo effect in an evolutionary logic related to the organism’s resource allocation decisions (Trimmer et al., 2013) and evaluating the evolutionary roots of psychological healing for global mental health services (Kohrt et al., 2020) are noteworthy. In mental health, the discussion of the paradox of common, heritable disorders through evolutionary genetic models (Keller & Miller, 2006), the linking of evolutionary theory with psychiatry and psychopharmacology (Stein, 2006), the joint evaluation of the functional and dysfunctional aspects of social anxiety (Maner & Kenrick, 2010), the placement of bipolar disorder within a psychoneuroimmunological framework (Rantala et al., 2021) and the proposal of a methodological framework for systematically testing evolutionary hypotheses (Hunt & Jaeggi, 2025) show that the field is maturing. The integration of biopsychosocial approaches with evolutionary theory was proposed early (Gilbert, 1995); the evolutionary foundations of compassion focused therapy have been elaborated (Gilbert, 2014); the clinical significance of loneliness has been documented (Heinrich & Gullone, 2006); suicide has been treated as an evolutionary puzzle (Aubin et al., 2013); the relationship between maternal age and postpartum depression has been reappraised through evolutionary theory (Bottino et al., 2012); and the possibility that interventions in childbirth interrupt an adapted pattern of consciousness has been discussed (Dahan, 2021). The analysis showing that the acute stress response forms a spectrum including freezing and fainting (Bracha, 2004) is also important for critical incident management.

11.2. Managerial implications

The first implication is that pain assessment protocols must be structurally protected against observer bias. Standard tools centred on the patient’s own report are less open to this bias than assessments based on observer coding; consistency audits across units can be monitored as a quality indicator.

The second implication is that the problem of overtesting and overdiagnosis cannot be explained solely by defensive medicine and financial incentives. Given the asymmetry of error management, the way to change testing behaviour lies not in persuading the clinician but in changing, at the institutional level, the perceived cost balance of the two error types. A structure of shared responsibility in which the clinician is not left alone in the case of a false negative directly affects this balance.

The third implication is that the treatment context should be treated as a component of the clinical outcome. The evolutionary interpretation of the placebo effect implies that the quality of patient-clinician interaction and the expectation of recovery produce measurable effects; this means that patient experience indicators may carry clinical meaning beyond being a satisfaction measure.

The fourth implication concerns post-incident reviews. Knowing that the acute stress response includes freezing requires that judgements about the performance of a staff member who remained motionless during an incident be made carefully; standard root cause analysis templates often fail to take this physiological reality into account.

11.3. Testable research questions for Türkiye

• RQ11.1. In emergency departments, do pain severity assessment and the administration of analgesia vary systematically by patient sex and the presence of a companion? Is this difference reduced in protocols based on patient self-report?
• RQ11.2. Is clinicians’ test-ordering behaviour significantly related to the asymmetry between the perceived professional costs of false negative and false positive errors, when these are measured?
• RQ11.3. Do decision support structures in which responsibility is shared at team level reduce unnecessary testing more than clinician-level feedback does?
• RQ11.4. Are patient experience indicators (time spent with the physician, quality of explanation, continuity) related to clinical outcome indicators independently of satisfaction?
• RQ11.5. In post-incident reviews, do managerial judgements about staff who remained motionless during the incident change when information is provided about the acute stress response spectrum?
• RQ11.6. Are continuous support and the intensity of intervention during labour related to the birth experience and postpartum psychological symptoms?

12. Physical Environment and Facility Design

12.1. State of evidence

The biophilia hypothesis, which holds that the human mind responds to natural environmental cues in a stress-reducing way, has been evaluated in the context of the effect of views of nature on health and well-being (Grinde & Patil, 2009); the benefits of nature experiences have been comprehensively reviewed (Franco et al., 2017); and a conceptual framework has been proposed for turning biophilia as an evolutionary adaptation into design principles (Barbiero & Berto, 2021).

The state of evidence in this area requires careful language. The evolutionary assumptions of restorative environments research have been critically analysed, and it has been argued that attributing positive responses to nature entirely to evolutionary origins is problematic (Joye & van den Berg, 2011). The area is therefore strong on the existence of the effect but contested on its mechanism. This distinction is critical for managerial decisions: to justify a design decision it is sufficient to demonstrate the effect; it is not necessary to prove that the mechanism is evolutionary.

12.2. Managerial implications

The first implication is that contact with nature in hospital design should be treated not as a cost item but as an intervention with measurable outputs. Waiting areas, staff rest spaces and window views in patient rooms can be evaluated through stress indicators and satisfaction.

The second implication is the effect of spatial arrangement on perceived safety. Lines of sight, visibility of escape routes and crowding are variables that directly affect threat appraisal systems; the design of emergency department waiting areas is therefore a matter not only of flow efficiency but also of tension management.

The third implication is the need to define these variables at the contract stage in large hospitals built under the public-private partnership model; post-construction improvement is both more costly and less effective than arrangements made at the design stage.

12.3. Testable research questions for Türkiye

• RQ12.1. In city hospitals, are there differences in staff stress indicators and patient satisfaction between units with different access to views of nature or indoor greenery?
• RQ12.2. Are crowding and restricted lines of sight in emergency department waiting areas related to the frequency of verbal assaults?
• RQ12.3. Is access to daylight in intensive care units related to the incidence of delirium and length of stay?
• RQ12.4. Does a quasi-experimental intervention adding elements of nature to staff rest areas produce a significant difference in end-of-shift exhaustion measures?

13. Digital Health and Technology Acceptance

13.1. State of evidence

It has been argued explicitly that evolutionary psychology can enrich technology acceptance theories (Abraham et al., 2013). The core argument is that users’ responses to new technologies relate not only to perceived usefulness and ease of use but also to deeper systems such as trust, status and threat appraisal. The potential of wearable biosensor systems in the context of resilience has also been discussed (Drury, 2014), and how digital platforms shape conceptions of well-being has been critically analysed (Docherty, 2020).

In terms of evidence maturity this is the weakest link, and this must be acknowledged openly. Most existing studies are at the level of conceptual proposals; testing acceptance models that include evolutionary constructs with large samples is still limited. This makes it possible to see the area not as a weakness but as an opportunity for an original contribution.

13.2. Managerial implications

The first implication is that resistance to telehealth should not be reduced to a lack of technical competence. The narrowing of the rich set of signals carried by face-to-face interaction (facial expression, tone of voice, mutual attention) in screen-mediated communication can directly affect the process of building trust. This requires adding variables of trust and signal richness to acceptance models.

The second implication concerns resistance on the clinician side. The adoption of decision support systems may be related to the perception that the system threatens the clinician’s professional status. In that case what is decisive is not user training, but presenting the system in a position that supports rather than replaces the clinician.

The third implication is the risk that continuous monitoring technologies produce a perception of surveillance; while visibility of contributions increases cooperation, asymmetric and one-way monitoring in the same system can trigger a threat response.

13.3. Testable research questions for Türkiye

• RQ13.1. In the acceptance of telehealth services, does adding trust and perceived signal richness to the standard acceptance model variables produce a significant increase in explained variance?
• RQ13.2. Is the adoption of clinical decision support systems negatively related to perceived threat to professional status? Does this relationship differ by physician seniority?
• RQ13.3. Is the intensity of use of personal health records such as e-Nabız related to a future-oriented time perspective?
• RQ13.4. In applications with continuous monitoring, does designing monitoring to be reciprocal (clinician and patient can see each other) increase sustained use compared with one-way monitoring?

14. A Common Methodological Framework

Grounding the research questions above on a common methodological basis is decisive for the cumulative progress of the field. Four points should be underlined.

First, testing an evolutionary hypothesis is different from assuming an evolutionary explanation. Methodological frameworks have been developed for testing evolutionary hypotheses systematically and in a way that allows them to be eliminated (Hunt & Jaeggi, 2025), and adapting these frameworks to health management research will protect the field from the criticism of producing retrospective narratives. The practical criterion is clear: an evolutionary framework should be used only when it provides a falsifiable prediction that existing theories cannot produce.

Second, cross-cultural comparison is central to this programme. Criteria for how claims of psychological universality can be tested have been set out in detail (Norenzayan & Heine, 2005). Türkiye, with its cultural position of neither fully overlapping with nor fully diverging from Western samples, offers an informative setting for testing claims of universality. Whether an effect replicates in Turkish samples is not merely a local finding but a theoretical contribution.

Third, the measurement problem must be taken seriously. Constructs such as life history strategy, disgust sensitivity and status threat should be measured with instruments adapted into Turkish and whose psychometric properties have been reported; tests conducted without adaptation studies make negative results uninterpretable. The first step of the research agenda is therefore to build the measurement infrastructure.

Fourth, the hierarchy of designs should be respected. Cross-sectional surveys are suitable for discovering associations but do not support causal claims. Vignette experiments are a powerful tool for judgement processes such as the deservingness heuristic and stigma. In-hospital randomised trials and quasi-experimental before-and-after designs should be preferred for intervention-focused questions such as hand hygiene, facility design and appointment arrangements. Table 3 summarises the proposed research agenda according to these criteria.

Table 3. Proposed research agenda by area of intersection

Area	Independent / explanatory construct	Outcome variable	Possible data source
Infection control	Disgust sensitivity, salience of contamination cues	Hand hygiene compliance (observed), isolation compliance	In-hospital observation plus survey, hospital infection records
Human resources	Status threat, belonging, perceived fairness	Burnout, turnover intention, intention to emigrate	Cross-sectional or panel survey of healthcare workers
Health policy	Deservingness heuristic, coalitional signals	Public support for service prioritisation	Vignette experiment, representative public opinion survey
Health behaviour	Life history strategy, future discounting	Screening participation, treatment adherence	TurkStat Health Survey plus complementary measurement
Clinical decision	Error management asymmetry, signal reading	Diagnostic bias, pain assessment	Vignette experiment, clinical record review
Facility design	Contact with nature, spatial safety cues	Stress, recovery time, satisfaction	Quasi-experimental before-and-after design
Digital health	Trust, status, threat appraisal	Telehealth acceptance, sustained use	PLS-SEM based acceptance model survey

15. Limitations and Closing Remarks

This research agenda should be read with three limitations. First, evolutionary explanations do not replace institutional, cultural and economic explanations. The strongest reason for low hand hygiene compliance in a hospital may be disruption in the supply of gloves and sanitiser; the evolutionary framework claims no superiority in explaining such proximate causes. Its contribution is to make sense of the persistent patterns that remain after the supply problem has been solved.

Second, the risk of the naturalistic fallacy applies at every step of this agenda. The existence of an evolutionary explanation for status hierarchy, stigma or intergroup avoidance does not legitimise them. On the contrary, knowing the contextual conditions under which they are strengthened is a prerequisite for producing designs that weaken them. The comprehensive proposal that intentional change at the institutional level can be grounded in evolutionary principles (Wilson et al., 2014) forms the normative basis of this agenda.

Third, the difference in evidence maturity between areas must be preserved. While disease avoidance and status threat have a relatively established evidence base, the evolutionary extension of technology acceptance is still at the level of proposal. Erasing this difference in managerial decision language would damage trust in the framework as a whole.

With these caveats, the agenda set out here holds a concrete promise for health management. The field’s most persistent problems (inequality in preventive services, the persistence of burnout, fluctuations in protocol compliance, stigma and vaccine hesitancy) share a common feature: they all persist not because people do not know, but because people respond predictably in particular contexts. Understanding the architecture of these responses is a prerequisite for designing the contexts that will change them. With its extensive administrative data infrastructure, family medicine records and nationally conducted surveys, the Turkish health system offers a favourable setting in which these tests can be carried out.

The real opportunity here is not to replicate international findings in a local sample, but to generate new questions that arise from the specificity of the local structure.

How to cite

This article is the English version of the technical report: Kurutkan, M. N. (2026). Eski bir zihin, yeni bir sağlık sistemi: Sağlık yönetimi için evrimsel psikolojiye giriş (Technical report). Düzce University, Department of Health Management. https://www.researchgate.net/publication/411199787

References

Abraham, C., Boudreau, M.-C., Junglas, I., & Watson, R. (2013). Enriching our theoretical repertoire: The role of evolutionary psychology in technology acceptance. European Journal of Information Systems, 22(1), 56–75. https://doi.org/10.1057/ejis.2011.25

Ahlstrom, B., Dinh, T., Haselton, M. G., & Tomiyama, A. J. (2017). Understanding eating interventions through an evolutionary lens. Health Psychology Review, 11(1), 72–88. https://doi.org/10.1080/17437199.2016.1260489

Aubin, H.-J., Berlin, I., & Kornreich, C. (2013). The evolutionary puzzle of suicide. International Journal of Environmental Research and Public Health, 10(12), 6873–6886. https://doi.org/10.3390/ijerph10126873

Aunger, R., & Curtis, V. (2016). Behaviour centred design: Towards an applied science of behaviour change. Health Psychology Review, 10(4), 425–446. https://doi.org/10.1080/17437199.2016.1219673

Barbiero, G., & Berto, R. (2021). Biophilia as evolutionary adaptation: An onto- and phylogenetic framework for biophilic design. Frontiers in Psychology, 12, Article 700709. https://doi.org/10.3389/fpsyg.2021.700709

Blaker, N. M., Rompa, I., Dessing, I. H., Vriend, A. F., Herschberg, C., & van Vugt, M. (2013). The height leadership advantage in men and women: Testing evolutionary psychology predictions about the perceptions of tall leaders. Group Processes & Intergroup Relations, 16(1), 17–27. https://doi.org/10.1177/1368430212437211

Blease, C. (2012). Mental health illiteracy? Perceiving depression as a disorder. Review of General Psychology, 16(1), 59–69. https://doi.org/10.1037/a0026494

Bottino, M. N., Nadanovsky, P., Moraes, C. L., Reichenheim, M. E., & Lobato, G. (2012). Reappraising the relationship between maternal age and postpartum depression according to the evolutionary theory: Empirical evidence from a survey in primary health services. Journal of Affective Disorders, 142(1–3), 219–224. https://doi.org/10.1016/j.jad.2012.04.030

Boyer, P., Firat, R., & van Leeuwen, F. (2015). Safety, threat, and stress in intergroup relations: A coalitional index model. Perspectives on Psychological Science, 10(4), 434–450. https://doi.org/10.1177/1745691615583133

Boykin, K., Brown, M., Macchione, A. L., Drea, K. M., & Sacco, D. F. (2021). Noncompliance with masking as a coalitional signal to US conservatives in a pandemic. Evolutionary Psychological Science, 7(3), 232–238. https://doi.org/10.1007/s40806-021-00277-x

Boysen, G. A., Morton, J., & Nieves, T. (2019). Mental illness as a relationship dealbreaker. Stigma and Health, 4(4), 421–428. https://doi.org/10.1037/sah0000157

Bracha, H. S. (2004). Freeze, flight, fight, fright, faint: Adaptationist perspectives on the acute stress response spectrum. CNS Spectrums, 9(9), 679–685. https://doi.org/10.1017/S1092852900001954

Campbell, L., Simpson, J. A., Stewart, M., & Manning, J. G. (2002). The formation of status hierarchies in leaderless groups: The role of male waist-to-hip ratio. Human Nature, 13(3), 345–362. https://doi.org/10.1007/s12110-002-1019-0

Clay, R. (2017). The behavioral immune system and attitudes about vaccines: Contamination aversion predicts more negative vaccine attitudes. Social Psychological and Personality Science, 8(2), 162–172. https://doi.org/10.1177/1948550616664957

Curtis, V., de Barra, M., & Aunger, R. (2011). Disgust as an adaptive system for disease avoidance behaviour. Philosophical Transactions of the Royal Society B: Biological Sciences, 366(1563), 389–401. https://doi.org/10.1098/rstb.2010.0117

Dåderman, A. M., & Basinska, B. A. (2021). Evolutionary benefits of personality traits when facing workplace bullying. Personality and Individual Differences, 177, Article 110849. https://doi.org/10.1016/j.paid.2021.110849

Dahan, O. (2021). Obstetrics at odds with evolution: The consequences of interrupting adaptive birthing consciousness. New Ideas in Psychology, 63, Article 100903. https://doi.org/10.1016/j.newideapsych.2021.100903

Danielsbacka, M., Tanskanen, A. O., Coall, D. A., & Jokela, M. (2019). Grandparental childcare, health and well-being in Europe: A within-individual investigation of longitudinal data. Social Science & Medicine, 230, 194–203. https://doi.org/10.1016/j.socscimed.2019.03.031

Docherty, N. (2020). Facebook’s ideal user: Healthy habits, social capital, and the politics of well-being online. Social Media + Society, 6(2), Article 2056305120915606. https://doi.org/10.1177/2056305120915606

Doogan, N. J., & Warren, K. (2017). A network of helping: Generalized reciprocity and cooperative behavior in response to peer and staff affirmations and corrections among therapeutic community residents. Addiction Research & Theory, 25(3), 243–250. https://doi.org/10.1080/16066359.2016.1249864

Drury, R. L. (2014). Wearable biosensor systems and resilience: A perfect storm in health care? Frontiers in Psychology, 5, Article 853. https://doi.org/10.3389/fpsyg.2014.00853

Ellis, B. J., Bates, J. E., Dodge, K. A., Fergusson, D. M., Horwood, L. J., Pettit, G. S., & Woodward, L. (2003). Does father absence place daughters at special risk for early sexual activity and teenage pregnancy? Child Development, 74(3), 801–821. https://doi.org/10.1111/1467-8624.00569

Ellis, B. J., & Boyce, W. T. (2008). Biological sensitivity to context. Current Directions in Psychological Science, 17(3), 183–187. https://doi.org/10.1111/j.1467-8721.2008.00571.x

Fitzgerald, C. J., & Danner, K. M. (2012). Evolution in the office: How evolutionary psychology can increase employee health, happiness, and productivity. Evolutionary Psychology, 10(5), 770–781. https://doi.org/10.1177/147470491201000502

Folwarczny, M., Otterbring, T., Sigurdsson, V., Tan, L. K. L., & Li, N. P. (2023). Old minds, new marketplaces: How evolved psychological mechanisms trigger mismatched food preferences. Evolutionary Behavioral Sciences, 17(1), 93–101. https://doi.org/10.1037/ebs0000288

Franco, L. S., Shanahan, D. F., & Fuller, R. A. (2017). A review of the benefits of nature experiences: More than meets the eye. International Journal of Environmental Research and Public Health, 14(8), Article 864. https://doi.org/10.3390/ijerph14080864

Garcia, H. A. (2024). Evolutionary roots of occupational burnout: Social rank and belonging. Adaptive Human Behavior and Physiology, 10(1), 50–70. https://doi.org/10.1007/s40750-024-00235-4

Gilbert, P. (1995). Biopsychosocial approaches and evolutionary theory as aids to integration in clinical psychology and psychotherapy. Clinical Psychology & Psychotherapy, 2(3), 135–156. https://doi.org/10.1002/cpp.5640020302

Gilbert, P. (2014). The origins and nature of compassion focused therapy. British Journal of Clinical Psychology, 53(1), 6–41. https://doi.org/10.1111/bjc.12043

Goodwin, R., Haque, S., Hassan, S. B. S., & Dhanoa, A. (2011). Representations of swine flu: Perspectives from a Malaysian pig farm. Public Understanding of Science, 20(4), 477–490. https://doi.org/10.1177/0963662510392484

Grinde, B., & Patil, G. G. (2009). Biophilia: Does visual contact with nature impact on health and well-being? International Journal of Environmental Research and Public Health, 6(9), 2332–2343. https://doi.org/10.3390/ijerph6092332

Gutiérrez, F., Peri, J. M., Torres, X., Caseras, X., & Valdés, M. (2007). Three dimensions of coping and a look at their evolutionary origin. Journal of Research in Personality, 41(5), 1032–1053. https://doi.org/10.1016/j.jrp.2007.01.006

Heinrich, L. A., & Gullone, E. (2006). The clinical significance of loneliness: A literature review. Clinical Psychology Review, 26(6), 695–718. https://doi.org/10.1016/j.cpr.2006.04.002

Hornsey, M. J., Bierwiaczonek, K., Sassenberg, K., & Douglas, K. M. (2023). Individual, intergroup and nation-level influences on belief in conspiracy theories. Nature Reviews Psychology, 2(2), 85–97. https://doi.org/10.1038/s44159-022-00133-0

Huang, J. Y., Sedlovskaya, A., Ackerman, J. M., & Bargh, J. A. (2011). Immunizing against prejudice: Effects of disease protection on attitudes toward out-groups. Psychological Science, 22(12), 1550–1556. https://doi.org/10.1177/0956797611417261

Hunt, A., Merola, G. P., Carpenter, T., & Jaeggi, A. V. (2024). Evolutionary perspectives on substance and behavioural addictions: Distinct and shared pathways to understanding, prediction and prevention. Neuroscience & Biobehavioral Reviews, 159, Article 105603. https://doi.org/10.1016/j.neubiorev.2024.105603

Hunt, A. D., & Jaeggi, A. V. (2025). The DCIDE framework: Systematic investigation of evolutionary hypotheses, exemplified with autism. Biological Reviews, 100(4), 1484–1511. https://doi.org/10.1111/brv.70010

Ivanov, L., Eisend, M., Diehl, S., Wang, I. M., & Karpinska-Krakowiak, M. (2021). Lethal versus reproductive disease appeals in preventive health advertising: The moderating effect of life history strategy. International Journal of Advertising, 40(4), 657–681. https://doi.org/10.1080/02650487.2020.1859170

Jefferson, A. (2017). Born to be biased? Unrealistic optimism and error management theory. Philosophical Psychology, 30(8), 1159–1175. https://doi.org/10.1080/09515089.2017.1370085

Jensen, C., & Petersen, M. B. (2017). The deservingness heuristic and the politics of health care. American Journal of Political Science, 61(1), 68–83. https://doi.org/10.1111/ajps.12251

Joye, Y., & van den Berg, A. (2011). Is love for green in our genes? A critical analysis of evolutionary assumptions in restorative environments research. Urban Forestry & Urban Greening, 10(4), 261–268. https://doi.org/10.1016/j.ufug.2011.07.004

Kavanagh, P. S., & Kahl, B. L. (2018). Are expectations the missing link between life history strategies and psychopathology? Frontiers in Psychology, 9, Article 89. https://doi.org/10.3389/fpsyg.2018.00089

Keller, M. C., & Miller, G. (2006). Resolving the paradox of common, harmful, heritable mental disorders: Which evolutionary genetic models work best? Behavioral and Brain Sciences, 29(4), 385–404. https://doi.org/10.1017/S0140525X06009095

Kohrt, B. A., Ottman, K., Panter-Brick, C., Konner, M., & Patel, V. (2020). Why we heal: The evolution of psychological healing and implications for global mental health. Clinical Psychology Review, 82, Article 101920. https://doi.org/10.1016/j.cpr.2020.101920

Laran, J., & Salerno, A. (2013). Life-history strategy, food choice, and caloric consumption. Psychological Science, 24(2), 167–173. https://doi.org/10.1177/0956797612450033

Lende, D. H., & Smith, E. O. (2002). Evolution meets biopsychosociality: An analysis of addictive behavior. Addiction, 97(4), 447–458. https://doi.org/10.1046/j.1360-0443.2002.00022.x

Li, N. P., Yong, J. C., & van Vugt, M. (2020). Evolutionary psychology’s next challenge: Solving modern problems using a mismatch perspective. Evolutionary Behavioral Sciences, 14(4), 362–367. https://doi.org/10.1037/ebs0000207

Little, A. C., & Roberts, S. C. (2012). Evolution, appearance, and occupational success. Evolutionary Psychology, 10(5), 782–801. https://doi.org/10.1177/147470491201000503

Maner, J. K., Dittmann, A., Meltzer, A. L., & McNulty, J. K. (2017). Implications of life-history strategies for obesity. Proceedings of the National Academy of Sciences, 114(32), 8517–8522. https://doi.org/10.1073/pnas.1620482114

Maner, J. K., & Kenrick, D. T. (2010). When adaptations go awry: Functional and dysfunctional aspects of social anxiety. Social Issues and Policy Review, 4(1), 111–142. https://doi.org/10.1111/j.1751-2409.2010.01019.x

Milkowska, K., Galbarczyk, A., Mijas, M., & Jasienska, G. (2021). Disgust sensitivity among women during the COVID-19 outbreak. Frontiers in Psychology, 12, Article 622634. https://doi.org/10.3389/fpsyg.2021.622634

Neuberg, S. L., Kenrick, D. T., & Schaller, M. (2011). Human threat management systems: Self-protection and disease avoidance. Neuroscience & Biobehavioral Reviews, 35(4), 1042–1051. https://doi.org/10.1016/j.neubiorev.2010.08.011

Norenzayan, A., & Heine, S. J. (2005). Psychological universals: What are they and how can we know? Psychological Bulletin, 131(5), 763–784. https://doi.org/10.1037/0033-2909.131.5.763

Norton, J. O., Evans, K. C., Semchenko, A. Y., Al-Shawaf, L., & Lewis, D. M. G. (2021). Why do people (not) engage in social distancing? Proximate and ultimate analyses of norm-following during the COVID-19 pandemic. Frontiers in Psychology, 12, Article 648206. https://doi.org/10.3389/fpsyg.2021.648206

Otterbring, T. (2020). Appetite for destruction: Counterintuitive effects of attractive faces on people’s food choices. Psychology & Marketing, 37(11), 1451–1464. https://doi.org/10.1002/mar.21257

Petersen, M. B., Sznycer, D., Cosmides, L., & Tooby, J. (2012). Who deserves help? Evolutionary psychology, social emotions, and public opinion about welfare. Political Psychology, 33(3), 395–418. https://doi.org/10.1111/j.1467-9221.2012.00883.x

Rantala, M. J., Luoto, S., Borráz-León, J. I., & Krams, I. (2021). Bipolar disorder: An evolutionary psychoneuroimmunological approach. Neuroscience & Biobehavioral Reviews, 122, 28–37. https://doi.org/10.1016/j.neubiorev.2020.12.031

Sell, A., Sznycer, D., Al-Shawaf, L., Lim, J., Krauss, A., Feldman, A., Rascanu, R., Sugiyama, L., Cosmides, L., & Tooby, J. (2017). The grammar of anger: Mapping the computational architecture of a recalibrational emotion. Cognition, 168, 110–128. https://doi.org/10.1016/j.cognition.2017.06.002

Sell, A., Tooby, J., & Cosmides, L. (2009). Formidability and the logic of human anger. Proceedings of the National Academy of Sciences, 106(35), 15073–15078. https://doi.org/10.1073/pnas.0904312106

Senior, C., Martin, R., Thomas, G., Topakas, A., West, M., & Yeats, R. M. (2012). Developmental stability and leadership effectiveness. The Leadership Quarterly, 23(2), 281–291. https://doi.org/10.1016/j.leaqua.2011.08.005

Stein, D. J. (2006). Evolutionary theory, psychiatry, and psychopharmacology. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 30(5), 766–773. https://doi.org/10.1016/j.pnpbp.2006.01.003

Sznycer, D., Tooby, J., Cosmides, L., Porat, R., Shalvi, S., & Halperin, E. (2016). Shame closely tracks the threat of devaluation by others, even across cultures. Proceedings of the National Academy of Sciences, 113(10), 2625–2630. https://doi.org/10.1073/pnas.1514699113

Timming, A. R. (2021). Why competitive productivity sometimes goes too far: A multilevel evolutionary model of karoshi. Cross Cultural & Strategic Management, 28(1), 96–107. https://doi.org/10.1108/CCSM-02-2020-0027

Trimmer, P. C., Marshall, J. A. R., Fromhage, L., McNamara, J. M., & Houston, A. I. (2013). Understanding the placebo effect from an evolutionary perspective. Evolution and Human Behavior, 34(1), 8–15. https://doi.org/10.1016/j.evolhumbehav.2012.07.004

Tybur, J. M., & Griskevicius, V. (2013). Evolutionary psychology: A fresh perspective for understanding and changing problematic behavior. Public Administration Review, 73(1), 12–22. https://doi.org/10.1111/puar.12003

Varella, M. A. C., Luoto, S., Soares, R. B. da S., & Valentova, J. V. (2021). COVID-19 pandemic on fire: Evolved propensities for nocturnal activities as a liability against epidemiological control. Frontiers in Psychology, 12, Article 646711. https://doi.org/10.3389/fpsyg.2021.646711

Vigil, J. M., & Coulombe, P. (2011). Biological sex and social setting affects pain intensity and observational coding of other people’s pain behaviors. Pain, 152(9), 2125–2130. https://doi.org/10.1016/j.pain.2011.05.019

Wilkinson, R. G., & Pickett, K. E. (2017). The enemy between us: The psychological and social costs of inequality. European Journal of Social Psychology, 47(1), 11–24. https://doi.org/10.1002/ejsp.2275

Williams, A. C. de C. (2002). Facial expression of pain: An evolutionary account. Behavioral and Brain Sciences, 25(4), 439–455. https://doi.org/10.1017/S0140525X02000080

Wilson, D. S., Hayes, S. C., Biglan, A., & Embry, D. D. (2014). Evolving the future: Toward a science of intentional change. Behavioral and Brain Sciences, 37(4), 395–416. https://doi.org/10.1017/S0140525X13001593

Yong, J. C., & Choy, B. K. C. (2021). Noncompliance with safety guidelines as a free-riding strategy: An evolutionary game-theoretic approach to cooperation during the COVID-19 pandemic. Frontiers in Psychology, 12, Article 646892. https://doi.org/10.3389/fpsyg.2021.646892

Young, S. G., Brown, M., & Sacco, D. F. (2021). Using psychological science to support social distancing: Tradeoffs between affiliation and disease-avoidance motivations. Social and Personality Psychology Compass, 15(5), Article e12597. https://doi.org/10.1111/spc3.12597

Zay Hta, M. K., Ting, R. S.-K., Goh, P. H., Gan, Q. H., & Jones, L. (2024). A systematic review on the cultural factors associated with stigma during pandemics. Current Psychology, 43(14), 12938–12969. https://doi.org/10.1007/s12144-023-04509-0

Key concepts in this article

Stigma Burnout Turnover intention Loneliness Telehealth Health belief model Waiting time Patient experience Informal care Technology acceptance model (TAM) Root cause analysis (RCA) Quality indicator Clinical decision support system Length of stay

Concepts link to the Glossary. Explore the knowledge graph →