The research is notable not just for its performance figures but for its interpretability-first philosophy. Rather than deploying a black-box neural network, the authors trained three classical, widely understood algorithms: Logistic Regression, Random Forest, and Support Vector Machine (SVM). Each model received the same task—classify respondents according to their level of psychosocial resilience based on a battery of psychological and behavioral variables. The Logistic Regression model emerged as the strongest performer, achieving 75.6 percent accuracy and an area under the ROC curve of 0.816, a measure of how well a model separates the two classes across all possible decision thresholds. The Random Forest classifier reached 72.6 percent accuracy, while the Support Vector Machine trailed slightly at 70.8 percent. These are not trivial differences. In a clinical screening context, where a model’s output might guide which staff members are offered early psychological support, a few percentage points of accuracy translate into fewer people missed and fewer false alarms.

The foundation of the study is the ‘How Right Now Mental Health & Coping’ dataset, collected by NORC at the University of Chicago between 2021 and 2022. It comprises 2,055 respondents in the United States, surveyed during a period when the emotional toll of the pandemic was still unfolding in real time. From this dataset, the researchers engineered a resilience index built on four psychometric variables: resilience itself, the ability to bounce back, sense of control, and confidence. Each respondent’s composite score was then dichotomized into two levels—high and low—using the median as the cutoff point. This binary framing is a pragmatic choice. While resilience exists on a continuum, converting it into a classification problem allows supervised learning algorithms to operate efficiently and allows results to be communicated clearly to occupational health practitioners who need actionable categories rather than abstract scores.

The feature importance analysis produced findings that read like a sobering diagnosis of pandemic-era mental health. Stress carried the largest weight in the model at 0.182, making it the single strongest predictor of low resilience. Depression followed closely at 0.160, and anxiety at 0.145. Together, these three negative emotional states dominated the predictive landscape, outweighing every other variable the researchers examined. Below them came hopelessness and changes in sleep patterns, both of which added further predictive signal. The ordering is intuitive but also clinically meaningful: it suggests that interventions aimed at reducing acute stress and treating depressive and anxious symptoms may do more to protect resilience than almost any other single measure. It also confirms, with quantitative rigor, what clinicians anecdotally observed during the pandemic—that the workers most at risk of psychological depletion were those drowning in stress, low mood, and sleep disruption simultaneously.

Not all predictive power flowed from negative states. The analysis also examined coping strategies—seeking social support, engaging in hobbies, prayer, and meditation—and found that they carried lower individual weights in the model but were consistently associated with greater resilience. What makes this finding compelling is the pattern of cumulativeness the authors observed. Respondents who reported using several coping strategies simultaneously had, on average, higher resilience scores than those who relied on a single strategy. The effect appears additive rather than synergistic: each additional coping behavior nudges the resilience index upward. This has practical implications for workplace mental health programs. Instead of prescribing one universal coping technique, occupational health departments might be better served by encouraging staff to build a diverse personal repertoire—combining social connection with restorative activities and contemplative practices—since the protective effect seems to compound.

Methodologically, the study follows a framework the authors describe as integrating real-world data, feature engineering, supervised machine learning, and behavioral analysis into a robust and interpretable predictive pipeline. The choice of interpretable models is deliberate and worth emphasizing. Logistic Regression, despite being one of the oldest algorithms in the statistical toolbox, produces coefficients that can be read directly as the influence of each variable on the predicted outcome. Random Forest, an ensemble of decision trees trained on random subsets of data and features, offers robustness to noise and nonlinear relationships while still allowing per-feature importance estimates. Support Vector Machines, which find the optimal hyperplane separating the two classes in a transformed feature space, round out the comparison. By benchmarking all three, the researchers could verify that their headline findings—the dominance of stress, depression, and anxiety—were not artifacts of any single algorithm’s inductive biases.

The area under the ROC curve of 0.816 for the Logistic Regression model deserves a closer look. An AUC of 0.5 would indicate performance no better than random guessing, while 1.0 represents perfect discrimination. A value of 0.816 places the model in the range generally considered good discrimination for behavioral and health prediction tasks, where human psychology introduces irreducible variance. Resilience, after all, is shaped by a web of factors—genetics, life history, socioeconomic conditions, workplace culture—that no survey dataset fully captures. Achieving reliable classification above 75 percent using self-reported psychological variables alone suggests that the measured constructs, particularly stress, depression, and anxiety, capture a substantial share of the signal. For screening purposes, this level of performance is comparable to instruments used to flag patients for further clinical evaluation, where the model’s role is triage rather than diagnosis.

The authors are candid about the limitations that bound their conclusions. The data represent a single point in time, a cross-sectional snapshot rather than a longitudinal record, which means the models capture associations at one moment and cannot track how resilience evolves as circumstances change. The measurements rest on participants’ self-reports, which are vulnerable to recall bias and social desirability effects. And the population sampled is drawn from the United States, which constrains how far the findings can be generalized to other healthcare systems and cultures—including, the authors note, those in Latin America, where healthcare workers faced distinct resource constraints and epidemic dynamics. As the next step, the team proposes validating the model with Latin American healthcare workers and enriching the feature set with additional measures, such as sleep data or physiological indicators, which could capture biological dimensions of stress that questionnaires miss.

Perhaps the most provocative suggestion in the paper is also the most modest one. The authors point out that models of this kind, built on existing data and simple interpretable algorithms, could be used by occupational health departments to identify people at risk at an early stage—without relying on complex artificial intelligence systems, expensive computational infrastructure, or proprietary black boxes. In an era when discussions of AI in medicine often gravitate toward frontier models and deep learning, this study is a reminder that logistic regression, trained on a well-constructed survey, can still deliver clinically useful discrimination. The pandemic demonstrated that healthcare workers’ psychological endurance is a system-level resource, one that protects both staff and patients. A tool that can flag, early and affordably, whose resilience is eroding—and pinpoint stress, depression, and anxiety as the primary culprits—offers health systems a way to intervene before exhaustion becomes exodus. The research team’s roadmap, extending validation across Latin American healthcare populations and incorporating physiological signals, points toward predictive mental health screening that is both globally relevant and methodologically transparent.

Subject of Research: Interpretable machine learning prediction of psychosocial resilience levels in healthcare workers using COVID-19 pandemic survey data

Article Title: A machine learning model distinguishes levels of psychological resilience in healthcare workers with 75% accuracy

Article References: A machine learning model distinguishes levels of psychological resilience in healthcare workers with 75% accuracy. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: machine learning, psychological resilience, healthcare workers, COVID-19, logistic regression, random forest, support vector machine, stress, depression, anxiety, occupational health, mental health prediction

The study rests on a deceptively simple question with substantial policy consequences. Turnover among educators of any kind is costly, but among inclusive preschool teachers the problem is particularly acute because these professionals combine general early childhood pedagogy with specialized skills for supporting children with diverse developmental needs. When they leave, programs that guarantee access to inclusive education lose institutional memory and specialized capacity that take years to rebuild. Prior research had established general associations between support and retention, but the mechanisms—the specific psychological pathways by which support translates into a decision to stay or go—remained underexplored, particularly for this population in China, where inclusive preschool education has expanded rapidly in recent years.

To unpack those mechanisms, the researchers administered validated measurement scales for four constructs: perceived social support, psychological resilience, role stress, and turnover intention. Perceived social support refers not merely to the amount of help available but to a person’s subjective sense that others—colleagues, administrators, family, friends—can be relied upon in times of need. Psychological resilience captures the capacity to adapt to and recover from adversity. Role stress, in this context, reflects the tension teachers experience when the demands, expectations, and ambiguities attached to their professional roles exceed their perceived capacity to meet them, including conflicts between the demands of inclusive practice and conventional classroom duties.

The analytical machinery behind the study was rigorous. After collecting survey data, the team conducted confirmatory factor analysis to verify that the measurement scales behaved as intended, and they assessed reliability using composite reliability scores and validity using average variance extracted, along with the heterotrait-monotrait ratio to check that the four constructs were statistically distinguishable from one another. They then tested their theoretical model using structural equation modeling, a technique that allows researchers to estimate a network of simultaneous relationships—here, paths from social support to resilience, to role stress, and to turnover intention—rather than examining each link in isolation. Because the educators were clustered across different provinces and institutions, the authors examined design effects and intraclass correlation coefficients to assess whether a multilevel modeling approach was warranted, and they checked variance inflation factors to rule out problematic collinearity among predictors.

The correlational results were striking in their consistency. Perceived social support correlated positively with psychological resilience at r = 0.57, a strong association by behavioral science standards, meaning educators who felt supported also reported markedly greater capacity to bounce back from workplace adversity. Social support correlated negatively with role stress at r = –0.54 and negatively with turnover intention at r = –0.46, both statistically significant at p < 0.001. In plain terms, teachers who perceived stronger support networks felt less torn by conflicting role demands and thought substantially less often about quitting. These bivariate patterns set the stage for the more demanding question: did resilience and role stress actually carry the effect of support onto turnover intentions, or was the relationship direct?

The mediation analysis answered decisively in favor of the indirect pathways. Using bootstrap procedures—a resampling method that yields robust confidence intervals for indirect effects—the researchers estimated a total standardized indirect effect of β = –0.41, with a 95 percent confidence interval ranging from –0.550 to –0.270, an interval clearly excluding zero. Perhaps most telling, this indirect effect accounted for 51.25 percent of the total effect of perceived social support on turnover intentions. When the two mediators were included in the model, the direct path from social support to turnover intention was no longer statistically significant, indicating full mediation: support does not appear to influence quit intentions through some mysterious residue, but through its measurable associations with a teacher’s inner reserves of resilience and outer burdens of role stress.

The theoretical scaffolding for this pattern draws on two well-established frameworks in occupational psychology. Conservation of resources theory holds that people strive to acquire and protect valued resources—material, social, and psychological—and that stress arises when those resources are threatened or lost. From this perspective, perceived social support functions as a resource reservoir: it replenishes the psychological capital, including resilience, that teachers spend coping with demanding classrooms. The job demands-resources model complements this view by distinguishing job demands, which consume energy and generate strain, from job resources, which fuel engagement and buffer strain. Social support is a quintessential job resource, and role stress is a classic strain response; the study’s mediational structure maps directly onto both frameworks, offering convergent evidence that the theories apply to inclusive early childhood settings.

The practical implications are concrete. If half of the effect of support on retention runs through reduced role stress, then interventions that clarify job descriptions, align expectations between administrators and classroom teachers, and provide adequate staffing and planning time for inclusive duties may amplify the benefits of supportive climates. If the other half runs through resilience, then programs that strengthen coping capacities—mentoring for early-career inclusive educators, peer networks, professional development that builds confidence with diverse learners—may consolidate the gains. The authors frame their conclusion cautiously but clearly: strengthening perceived social support may be linked to lower turnover intentions through associations with enhanced resilience and reduced role stress. Because the design is cross-sectional, measuring all variables at a single point in time, the study cannot prove causality; longitudinal and intervention studies would be needed to confirm that boosting support actually lowers quit intentions over time. Self-reported survey data also carry inherent limitations, and the sample, though large and geographically broad within China, may not generalize to all educational systems.

Even with those caveats, the study contributes a quantified, mechanism-level account of a workforce problem that has often been described only in general terms. For school leaders, the message is that support is not a soft nicety but a structural lever: its influence on whether inclusive preschool teachers stay on the job is large, statistically robust, and traceable to specific psychological channels. For researchers, the findings invite longitudinal designs that track educators over time, tests of whether resilience training and role clarification independently moderate the pathways identified here, and comparisons across countries with different models of inclusive education. As inclusive early childhood education continues to expand globally, understanding the psychology of retention in this specialized workforce may prove as important as training the teachers themselves.

Subject of Research: Psychological mechanisms linking perceived social support to turnover intentions among preschool inclusive educators, mediated by resilience and role stress.

Article Title: How does perceived social support influence turnover intentions? Resilience and role stress as mediators

Article References: Feng, X., & Xu, E. (2026). How does perceived social support influence turnover intentions? Resilience and role stress as mediators. BMC Psychology. https://doi.org/10.1186/s40359-026-05595-y

Image Credits: AI Generated

DOI: 10.1186/s40359-026-05595-y

Keywords: social support, psychological resilience, role stress, turnover intentions, preschool inclusive educators, mediation analysis, structural equation modeling, occupational psychology, conservation of resources theory, job demands-resources model, teacher retention, BMC Psychology

The research team, led by Erzsébet Stephens-Sarlós and Attila Szabo of Széchenyi István University in Győr, Hungary, recruited 115 community-dwelling adults aged 60 and over, with a mean age of 75.9 years. Participants were drawn from nine social associations for older adults and screened to exclude dizziness, cardiovascular disease, balance disorders, untreated hypertension, and previously diagnosed mental or behavioral disorders. Each volunteer underwent a standardized assessment of thirteen primitive reflexes on both sides of the body, including the symmetrical and asymmetrical tonic neck reflexes, the tonic labyrinthine reflex, the Galant, Moro, palmar grasp, sucking, Babkin, Babinski, and glabellar reflexes, the core tendon guard reflex, the Schilder test, and the vestibular-ocular reflex. A reflex was scored present if it appeared in at least four of six elicitation trials, and side totals were summed separately for the right and left body.

The rigor of the reflex assessment matters, because primitive reflex testing has long been dismissed as imprecise. The protocol used in the study had previously undergone formal psychometric validation, demonstrating near-perfect intra-rater and inter-rater agreement, with weighted Cohen’s kappa values ranging from 0.912 to 1.000. Cognitive functioning was gauged with the Mini-Mental State Examination, physical activity with the World Health Organization’s Global Physical Activity Questionnaire, and psychological resilience with the ten-item Connor-Davidson Resilience Scale. Data collection proceeded individually in quiet, familiar rooms, with one researcher administering the reflex battery and a second conducting the cognitive and questionnaire measures, and the study received ethical approval from the university’s research ethics board in accordance with the Declaration of Helsinki.

The headline findings are striking. In hierarchical linear regressions, right-sided primitive reflexes alone explained 41.5 percent of the variance in cognitive functioning and a remarkable 50.9 percent of the variance in psychological resilience. Adding left-sided reflexes and physical activity pushed the cognitive model to 57.4 percent explained variance and the resilience model to 66.2 percent, with age failing to contribute significant predictive power once the reflex and activity measures were accounted for. In a population of supposedly healthy older adults, no conventional demographic variable came close to this explanatory strength, which is precisely why the authors argue that primitive reflex assessment deserves a place in research on healthy aging and, potentially, in clinical screening.

Equally intriguing was the lateralization effect. The average number of reflexes on the right side of the body, 4.85, was significantly higher than the 3.99 observed on the left, and the right-sided total emerged as the dominant predictor in every model, contributing more than six times the explanatory variance of its left-sided counterpart. The authors speculate that this asymmetry may reflect hemisphere-specific aging of the cortex. Longitudinal MRI research has shown that the cerebral cortex is normally slightly thicker on the left, and that age-related cortical asymmetry progressively diminishes as the left hemisphere thins faster, a process accelerated in Alzheimer’s disease. Handedness may also play a role, since right-handedness favors stronger left-hemisphere motor control over the right side of the body. The authors caution, however, that no neuroimaging was performed and that this interpretation remains hypothesis-generating.

The study also examined whether primitive reflexes simply track age. The answer, surprisingly, is mostly no. Of the thirteen reflexes, only the symmetrical tonic neck reflex correlated significantly with age, and even that relationship explained less than 5 percent of the variance. Total right-sided reflex burden showed a weak positive association with age, accounting for roughly 6.6 percent of variance. This echoes the Maastricht Aging Study, which found the highest reflex prevalence in adults aged 66 to 82, and more recent work showing that about a third of adults aged 45 to 91 exhibit primitive reflexes that increase with age and accompany poorer cognitive task performance. But the new data suggest that reflex reappearance is not an inevitable calendar-driven process; it appears to index something more specific about the integrity of cortical inhibitory control.

That something, the authors argue, may be the progressive erosion of frontal networks. Primitive reflexes are normally suppressed as prefrontal and frontal regions mature, and their re-emergence in adulthood may signal reduced cortical command over phylogenetically older brainstem motor circuits. A 2024 meta-analysis found that primitive reflexes occurred 13.94 to 16.38 times more often in older adults with dementia than in healthy controls, with the grasp reflex most closely implicated. If persistent reflexes are the behavioral fingerprint of declining hierarchical sensorimotor regulation, then their strong inverse relationship with cognitive functioning, and now with psychological resilience, becomes biologically coherent rather than coincidental. Resilience itself is a robust predictor of health in later life, and the study is the first to link its erosion to the return of infantile motor patterns.

Physical activity added its own twist. Using mediation analysis, the researchers found that habitual physical activity statistically accounted for part of the association between primitive reflexes and both cognitive functioning and psychological resilience, exerting significant negative indirect effects that partially attenuated the reflexes’ adverse relationships with the outcome measures. In other words, staying active appears to buffer, though not eliminate, the damage signaled by returning reflexes. This aligns with a large body of evidence showing that physically active adults over 60 face lower risks of cognitive deterioration, dementia, and Alzheimer’s disease, and that even a 16-week functional fitness intervention can measurably raise psychological resilience in this age group. The mediation pattern suggests that sensorimotor integrity, physical activity, cognition, and resilience may be interconnected facets of a single neurobiological system rather than independent dimensions of healthy aging.

The authors are careful to temper these conclusions. The cross-sectional design precludes causal inference, and mediation in this context reflects statistical association, not proven mechanism. Physical activity was self-reported, leaving room for recall bias, and objective accelerometry would strengthen future work. The sample underrepresented men at 23.5 percent, was recruited from a limited set of community organizations, and recorded MMSE means below 24 for both sexes, a score that can indicate mild cognitive impairment, raising questions about how ‘healthy’ the sample truly was. Generalizability to institutionalized or socioeconomically diverse populations remains uncertain, and the authors stress that primitive reflex assessment should not be treated as a stand-alone diagnostic tool.

Nevertheless, the implications are hard to ignore. If validated longitudinally, a cheap, quick, and standardized reflex examination could give geriatricians, rehabilitation physicians, physiotherapists, and occupational therapists an early, low-cost window into cortical health that conventional cognitive screening misses, identifying older adults who might benefit from individualized movement-based interventions and closer monitoring of functional decline. The study also opens an evolutionary-flavored question that is likely to fascinate researchers and the public alike: the same brainstem circuits that help a newborn grasp its mother’s hand may, when they resurface in a 75-year-old, be telling us something profound about the aging brain, and about how much of that story regular movement can still rewrite. Larger, longitudinal studies integrating reflex assessment with brain imaging and objective activity monitoring are the clear next step.

Subject of Research: The reappearance of primitive reflexes in older adults and its relationship with cognitive functioning, psychological resilience, and physical activity

Article Title: Primitive Reflexes Predict Cognitive Functioning and Psychological Resilience in Older Adults: Physical Activity Mediates the Relationship

Article References: Stephens-Sarlós, E., Tóth, E. E., Alföldi, Z., Somogyi, A., Ihász, F., & Szabo, A. (2026). Primitive Reflexes Predict Cognitive Functioning and Psychological Resilience in Older Adults: Physical Activity Mediates the Relationship. Ageing International, 51(3), Article 32. https://doi.org/10.1007/s12126-026-09676-6

Image Credits: AI Generated

DOI: 10.1007/s12126-026-09676-6

Keywords: primitive reflexes, cognitive functioning, psychological resilience, physical activity, aging, brainstem reflexes, MMSE, mediation analysis, healthy aging, neurodegeneration, older adults, motor control

The concept at the center of the framework is known as stress generation. Unlike stressors that occur independently of a person’s actions, stress-generating events are at least partly shaped by an individual’s behaviour, relationships or decisions. Examples might include escalating an argument, withdrawing from social contact, failing to meet an obligation because of low motivation, or repeatedly seeking reassurance in ways that strain relationships. The researchers emphasize that this process is not deliberate and should not be interpreted as blaming people for their suffering. Instead, stress generation describes a dynamic interaction between symptoms and the social environment, in which psychological difficulties can alter behaviour and those behavioural changes can produce new sources of stress.

The proposed model begins with changes in affective functioning, the systems involved in emotional experience and regulation. Internalizing disorders are commonly associated with heightened negative affect, reduced positive affect, emotional reactivity and difficulty returning to an emotional baseline after distress. A person may become more sensitive to rejection, more easily overwhelmed by ordinary setbacks or less able to experience reward from social and daily activities. These affective alterations can influence how situations are interpreted and how quickly a person responds to them. A minor disagreement, for example, may feel unusually threatening, while a neutral message from a friend may be experienced as evidence of disapproval. Over time, such emotional shifts can shape patterns of action that affect relationships, work and other areas of life.

The next stage involves everyday behaviour. When people are experiencing persistent sadness, fear, irritability or emotional exhaustion, they may avoid demanding situations, reduce communication, postpone responsibilities or respond more intensely during interpersonal conflict. These behaviours can provide short-term relief. Avoiding a difficult conversation may temporarily reduce anxiety, and withdrawing from social contact may protect someone from immediate embarrassment or perceived rejection. However, the same strategies can create longer-term consequences, including missed deadlines, financial complications, social isolation or resentment from family members and colleagues. In this way, behaviour that is understandable in the moment can unintentionally increase the probability of future stress.

The model gives particular importance to cognitive vulnerability, which can determine whether an emotional or behavioural reaction remains contained or develops into a larger chain of events. Cognitive vulnerability includes persistent patterns such as negative interpretations, rumination, hopeless expectations and beliefs that stressful outcomes are inevitable. Rumination can keep attention fixed on a conflict long after it has ended, while threat-focused thinking can encourage defensive or avoidant responses. When these cognitive processes interact with strong negative emotion, a person may repeatedly revisit the same event, interpret ambiguous actions in the most damaging way and respond as though a feared outcome has already occurred. That response may then provoke real interpersonal difficulties, providing apparent confirmation of the original belief.

This sequence can create a feedback loop between the individual and the surrounding social environment. An emotionally distressed person may withdraw, communicate less clearly or react defensively. Other people may respond with frustration, criticism or reduced support. The individual can then perceive those reactions as further evidence of rejection or hostility, increasing emotional distress and making additional problematic behaviour more likely. The process is not necessarily confined to one relationship or one type of stressor. Repeated cycles may spread across friendships, family interactions, romantic relationships, academic settings and workplaces. As stress accumulates, the resulting burden may worsen symptoms, creating the conditions for another round of stress generation.

The researchers describe this process as transdiagnostic, meaning that it may operate across multiple internalizing conditions rather than belonging to a single diagnosis. Depression, generalized anxiety, social anxiety and related forms of psychopathology often differ in their specific symptoms, but they can share underlying features such as negative affect, avoidance, interpersonal sensitivity and repetitive negative thinking. These common factors may help explain why stress generation is linked to impairment across diagnostic categories. Instead of treating each disorder as an isolated cause of stress, the framework maps how shared emotional, behavioural and cognitive mechanisms can produce similar patterns of escalating difficulty in different people.

A major implication of the model is that stress generation should not be viewed as a single event with a single cause. It is better understood as a process unfolding over time. Affective disruption may increase the likelihood of a particular behaviour, the behaviour may alter the social environment, and the resulting consequences may activate cognitive vulnerabilities that intensify emotional symptoms. The timing and sequence of these steps are important. A brief episode of avoidance may have little effect in one context but lead to substantial stress when it occurs repeatedly, affects an important responsibility or is interpreted by others as indifference. This process-based perspective may help researchers identify which links in the chain are most influential for different individuals.

The framework also points to several possible intervention targets. Treatment could focus directly on emotional regulation, helping people reduce reactivity and recover more effectively after distress. Behavioural approaches could address avoidance, withdrawal, disrupted routines and communication patterns that increase the likelihood of conflict or missed obligations. Cognitive interventions could target rumination, catastrophic interpretations and rigid expectations about rejection or failure. Because the model describes a reciprocal relationship between individuals and their environments, interventions might also include interpersonal strategies designed to repair damaged relationships, increase constructive communication and strengthen access to social support. Interrupting even one part of the cycle could potentially prevent later stressors from developing.

The Perspective does not present stress generation as an unavoidable consequence of internalizing psychopathology, nor does it suggest that individuals are responsible for every stressful event they experience. Rather, it offers a mechanistic account of how symptoms and circumstances can influence one another, sometimes producing self-reinforcing patterns that prolong illness and increase impairment. By bringing affective, behavioural and cognitive processes into a single framework, the model may help explain why some people recover after a stressful episode while others enter a cycle of accumulating difficulties. It also shifts attention toward prevention: identifying early changes in emotion, behaviour or thinking could make it possible to intervene before ordinary problems escalate into major life stress. For a field increasingly focused on personalized and transdiagnostic treatment, understanding how stress is generated may become a crucial step toward breaking the feedback loops that keep internalizing disorders alive.

Subject of Research: Stress generation processes in internalizing disorders

Article Title: A process model of stress generation in internalizing disorders

Article References: Starr, L.R., Dozois, D.J.A., Rnic, K. et al. A process model of stress generation in internalizing disorders. Nature Reviews Psychology (2026). https://doi.org/10.1038/s44159-026-00607-5

Image Credits: AI Generated

DOI: 10.1038/s44159-026-00607-5

Keywords: stress generation, internalizing disorders, depression, anxiety, affective functioning, cognitive vulnerability, behavioural processes, rumination, interpersonal stress, mental health interventions