The study, conducted by Chia-Shan Wu of the National Tainan Junior College of Nursing and Shih-Yeh Chen of the Department of Engineering Science at National Cheng Kung University, was designed as an explanatory sequential mixed-methods trial. Between March and April 2026, 106 nursing students from two intact classes took part in a five-week intervention embedded in their psychiatric nursing education. Fifty-six students used an AI-assisted gamified mobile learning system, while fifty students used a comparable gamified system without AI support. The researchers measured three outcomes before and after the intervention: psychiatric nursing knowledge, attitudes toward mental illness as captured by the Mental Illness Clinicians’ Attitudes Scale, and reflective thinking.

The choice of outcomes reflects a long-standing frustration in mental health education. Students can memorize diagnostic criteria and pharmacological mechanisms, yet still carry the stigma, fear, and distance that have historically characterized psychiatric care. The World Health Organization has repeatedly emphasized that person-centred mental health services require practitioners who can engage with patients’ lived experience, not merely manage symptoms. Building that capacity, the researchers argue, requires more than lectures; it requires opportunities to practice perspective-taking and to reflect on one’s own assumptions in a safe environment.

That is where the AI component comes in. In the AI-assisted condition, students interacted with virtual patients and received feedback generated by artificial intelligence, which responded to their choices and prompted them to consider the patient’s point of view. The system was grounded in Self-Determination Theory, a psychological framework that holds that motivation flourishes when learners experience autonomy, competence, and relatedness. Gamified elements such as challenges and progression were intended to sustain engagement, while the AI layer was intended to make each interaction feel responsive and personal, simulating something closer to a real clinical conversation than a fixed multiple-choice scenario.

The quantitative results tell a nuanced story. Psychiatric nursing knowledge increased in both groups, and the researchers found no evidence that the AI-assisted group improved more than the control group on this measure. The effect size was small and statistically non-significant, with a p-value of .579 and a Cohen’s d of 0.11, with a 95 percent confidence interval spanning −0.27 to 0.49. In other words, when it comes to acquiring core psychiatric knowledge, a well-designed gamified learning system may be enough, and the AI layer did not add measurable advantage.

But the picture changed sharply for the more human dimensions of learning. Students in the AI-assisted condition showed significantly greater improvements in their attitudes toward people with mental illness, with a p-value of .041 and an effect size of d = 0.32, with a confidence interval of −0.06 to 0.71. The difference in reflective thinking was even more striking: the AI-assisted group improved substantially more, with p < .001 and d = 0.58, and a 95 percent confidence interval of 0.19 to 0.97, which lies entirely above zero. The researchers analyzed these longitudinal outcomes using generalized estimating equations, a statistical approach suited to repeated measurements in a quasi-experimental pretest–posttest control-group design.

Reflective thinking, the outcome with the strongest effect, is often described as the engine of professional growth in nursing. It is the habit of stepping back from an encounter, examining one’s own reactions and assumptions, and asking what the patient experienced and what could be done differently. The qualitative half of the study, based on semi-structured interviews with eight students, offered a window into why the AI system may have strengthened this habit. Students reported that the AI-generated feedback and the virtual patient interactions helped them think more deeply about their own responses and become more aware of how patients might perceive their words and behavior. In effect, the AI acted as a mirror, returning students’ decisions to them reframed through the patient’s perspective.

This mechanism matters because attitude change is notoriously difficult to teach. Traditional anti-stigma interventions often rely on contact with people with mental illness, which is powerful but logistically demanding and variable in quality. Simulated patients are expensive and inconsistent across settings. An AI-driven virtual patient, by contrast, can be available at any hour, on any phone, and can generate feedback tailored to each student’s specific choices. If such systems can reliably nudge attitudes in a more empathetic direction, they could offer nursing schools a scalable complement to clinical placements, particularly in regions where mental health training resources are scarce.

The authors are careful about the limits of their findings, and the caution is worth emphasizing. The study was a quasi-experiment using intact classes rather than random assignment, which means pre-existing differences between the two groups cannot be fully ruled out. The five-week intervention was short, the sample came from a single institution, and the interview subsample was small. Most importantly, the researchers explicitly note that the study does not establish improvements in person-centred care competence or in actual clinical practice. Improved attitudes and stronger reflective thinking are promising precursors to better care, but the chain from classroom simulation to bedside behavior remains to be demonstrated.

Even with those caveats, the study offers a concrete signal in a fast-moving field. Funded by Taiwan’s National Science and Technology Council through project NSTC 114-2410-H-439-004 and approved by the Human Research Ethics Committee of National Cheng Kung University, the trial suggests that the value of AI in health professional education may lie less in delivering facts, where textbooks and lectures already perform well, and more in cultivating the relational and reflective capacities that have always been the hardest to teach. As generative AI systems grow more capable of sustaining realistic, responsive dialogue, the possibility of giving every nursing student thousands of low-stakes practice conversations with virtual patients moves from speculation toward routine. If future trials confirm that these interactions translate into more compassionate clinical care, the humble gamified phone app tested in Tainan may come to be seen as an early prototype of how machines teach humans to be more human with patients.

Subject of Research: Preparing future nurses for person-centred mental health care through AI-assisted gamified learning: a mixed-methods study

Article Title: Preparing future nurses for person-centred mental health care through AI-assisted gamified learning: a mixed-methods study

Article References: Wu, C.-S., & Chen, S.-Y. (2026). Preparing future nurses for person-centred mental health care through AI-assisted gamified learning: a mixed-methods study. BMC Nursing. https://doi.org/10.1186/s12912-026-05488-w

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05488-w

Keywords: Preparing, future, nurses, person-centred, mental, health, care, AI-assisted, gamified, learning, mixed-methods, scientific research

The research, led by Mohd Zaidi Bin Amiruddin of Universitas Pendidikan Indonesia together with colleagues including Kristóf Fenyvesi of the Finnish Institute for Educational Research at the University of Jyväskylä, focused on a question that has occupied science education researchers for decades: how do you help a student not just memorize the right answer, but genuinely restructure the internal model they use to reason about a physical phenomenon? The team’s answer was a learning intervention they call RefTaR, short for refutational texts assisted by augmented reality, which embeds three-dimensional visualization directly into a text-based strategy for confronting misconceptions.

Refutational texts are a well-established tool in science education. Rather than simply presenting the correct explanation, a refutational text explicitly names the common misconception, acknowledges that it is intuitive and widespread, and then directly refutes it before offering the scientific account. Research stretching back over several decades has shown that this approach outperforms plain expository text when the goal is overturning deeply held intuitive ideas. The catch is that many physics misconceptions involve entities students cannot see at all. Telling a student that their idea about charge transfer is wrong is one thing; giving them something concrete to replace it with is another.

That is where the augmented reality component enters. The RefTaR materials incorporated a macro-micro model, explicitly linking the observable, macroscopic behavior of charged objects with the microscopic behavior of subatomic particles. Students reading the refutational passages could simultaneously view augmented reality renderings and three-dimensional video animations showing what happens at the level of electrons when objects are rubbed together, transferred charge redistributes, or neutral objects become polarized. The design reflects a broader principle from multimedia learning research: multiple representations are most effective when they are coordinated so that the learner can map one onto the other, rather than being presented as disconnected pieces of information.

To test whether this combination actually helped, the researchers ran a group-randomized alternative-treatments design with a pretest. Three available twelfth-grade classes were the pool, and two of them were randomly assigned to the experimental and control conditions. The experimental group, twenty-eight students, received the full RefTaR intervention. The control group, thirty-one students, received refutational texts alone, without the augmented reality and 3D video components. Both groups received the same total amount of instruction, 270 minutes delivered across two sessions, which means any difference in outcomes could be attributed to the nature of the materials rather than to extra time on task.

Measuring mental models is trickier than measuring test scores, and the team took a correspondingly careful approach. Students’ understanding was assessed across six static electricity subconcepts using an open-ended level-of-understanding test. Rather than forcing a binary right-or-wrong judgment, this instrument allowed the researchers to classify each student’s mental model into one of three categories: Initial, meaning the intuitive or everyday conception; Synthetic, a hybrid state where scientific and intuitive ideas coexist and compete; and Scientific, the fully accepted explanation. The open-ended responses were scored independently by three raters, who reached 97.9 percent agreement, corresponding to a Fleiss’ kappa of 0.98, an almost perfect level of inter-rater reliability that lends considerable weight to the classifications.

The results tell a nuanced story that the authors are careful not to overstate. When the researchers used Fisher’s exact tests to compare the proportion of students showing any improvement from pretest to posttest, the so-called acceptable change, they found no statistically significant difference between the RefTaR group and the refutational-text-only group. In other words, both interventions moved students in the right direction at roughly comparable rates, and the addition of augmented reality did not produce a general advantage in the overall likelihood of progress.

But when the researchers narrowed their focus to the most demanding outcome, the proportion of students who reached a fully scientific mental model at posttest, a clear and consistent difference emerged. Across all six static electricity subconcepts, the RefTaR group had a significantly higher proportion of students at the Scientific level, with risk differences ranging from 17.4 to 42.9 percentage points, all statistically significant. This pattern held for every subconcept examined, which is a striking degree of consistency for a classroom-based study with modest sample sizes.

The interpretation the authors offer is that RefTaR’s advantage lies specifically in the final, integrative stage of mental-model reconstruction. Moving a student from an Initial model to a Synthetic one, where scientific and intuitive ideas begin to compete, appears achievable with refutational text alone. But completing the journey, fully displacing the intuitive model so that the scientific one operates alone, seems to benefit from the kind of rich, coordinated visualization that augmented reality and 3D video provide. The microscopic world of electrons and charge distributions, rendered as manipulable three-dimensional objects overlaid on the student’s physical environment, may give learners the concrete anchor they need to consolidate a coherent scientific model rather than a fragile hybrid.

The study’s authors are candid about its limits and about the questions that remain. Because the RefTaR intervention bundled augmented reality with 3D video and a macro-micro representational structure, the design cannot isolate which specific element drove the advantage. It could be the immersive quality of AR itself, the dynamic nature of the animations, the explicit macro-micro linkage, or some synergy among all three. The researchers suggest that future work should separate the contribution of augmented reality from that of general visual support, a step that would sharpen the design principles for technology-based learning materials. They also note that the study involved a small number of intact classes, a common constraint in school-based research, and that the findings are associative rather than proof of a universal causal mechanism.

Even with those caveats, the study lands at a moment when schools worldwide are weighing whether emerging technologies justify their cost and complexity in the classroom. Augmented reality in education has generated enthusiasm for years, but rigorous, controlled comparisons with well-matched non-AR alternatives remain comparatively rare. This study contributes something valuable to that conversation: evidence that visualization technology may matter most not at the beginning of conceptual change, where good text design already does much of the work, but at the end, where the hardest cognitive step, full model replacement, takes place. For physics educators wrestling with topics like electrostatics, where the relevant entities are invisible and the intuitive models are stubborn, that is a practically useful insight. It suggests that the question is not simply whether to use augmented reality, but where in the learning sequence it earns its place, and what representational structure it should carry when it does.

Subject of Research: Augmented reality-assisted refutational texts with a macro-micro model to support secondary students’ mental models

Article Title: Augmented reality-assisted refutational texts with a macro-micro model to support secondary students’ mental models

Article References: Augmented reality-assisted refutational texts with a macro-micro model to support secondary students’ mental models. (n.d.). https://doi.org/10.1007/s43545-026-01788-9

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01788-9

Keywords: Augmented, reality-assisted, refutational, texts, macro-micro, model, support, secondary, students, mental, models, scientific research

The Wilson and Cleary framework, first proposed in the 1990s and later revised, is one of the most influential conceptual models in outcomes research. It links biological and physiological variables through symptom status and functional health to general health perceptions and, ultimately, overall quality of life. What makes the model useful is its insistence on pathways: it does not simply ask which factors correlate with quality of life, but proposes directional chains through which disease mechanisms are translated into lived experience. Applying this revised model to Parkinson’s disease allowed the study’s authors to test, in a structured and statistically explicit way, which determinants carry the greatest weight along that chain.

Parkinson’s disease is the second most common neurodegenerative disorder worldwide, and its non-motor burden is increasingly recognized as a dominant driver of disability. Depression, anxiety, apathy, sleep disturbance, pain, fatigue, and sexual dysfunction are all more prevalent in Parkinson’s than in age-matched populations, and each has been independently associated with poorer quality of life in prior work. What has been missing, the researchers argue, is an integrated, multidimensional account that places these factors in a single explanatory architecture, rather than treating them as isolated contributors measured one at a time.

The study assembled a battery of validated instruments capturing the model’s successive levels: physiological measures and clinical characteristics of the disease, symptom scales covering both motor and non-motor domains, functional assessments, mental health measures including depressive and anxiety symptoms, and dedicated evaluation of sexual health. Quality of life was assessed with disease-specific and generic instruments, allowing the authors to examine both overall health perceptions and broader life evaluation. Statistical modeling then quantified the direct and indirect effects of each determinant, revealing how influences propagate from biological dysfunction through symptoms and function to the way patients judge their own health.

The headline finding is striking in its simplicity: mental health and sexual function emerged as determinants of quality of life comparable in importance to, and in some pathways stronger than, the classical motor measures that dominate routine neurological assessment. Depressive symptoms in particular acted as a powerful hub, both directly reducing quality of life and mediating the effects of other clinical variables. Sexual dysfunction, which affects a substantial proportion of both men and women with Parkinson’s disease, contributed significantly to poorer health perceptions even after accounting for disease severity, age, and functional status. The authors emphasize that these are not peripheral complaints but central components of the patient experience.

The pathway structure of the revised model proved illuminating. Motor severity influenced quality of life partly through its effect on symptoms and daily functioning, but also through its impact on mood, illustrating an indirect route in which physical decline erodes emotional well-being, which in turn depresses overall life evaluation. Conversely, some non-motor symptoms exerted effects that were largely mediated by mental health, suggesting that psychological state functions as a gateway through which much of the disease’s subjective burden passes. This mediation pattern has practical implications: interventions targeting depression and anxiety may ameliorate the quality-of-life consequences of a range of otherwise distinct symptoms.

Sexual health deserves particular attention in this framework. Sexual dysfunction in Parkinson’s disease arises from a convergence of mechanisms, including dopaminergic degeneration affecting autonomic and limbic circuits, medication effects, motor limitations, relationship strain, and psychological factors. The study’s finding that sexual function retains a significant association with quality of life within a multidimensional model underscores that it is an independent dimension of health, not merely a byproduct of depression or disability. Yet sexual health remains one of the least discussed topics in routine Parkinson’s care, with patients frequently reluctant to raise it and clinicians rarely asking. The authors suggest that structured assessment of sexual function should be incorporated into comprehensive care pathways.

The multidimensional approach also highlights the interdependence of domains often managed by different specialists. Sleep problems feed fatigue; fatigue erodes physical activity; inactivity worsens mood and function; and each step along the chain degrades health perceptions. By modeling these relationships simultaneously, the revised Wilson and Cleary framework provides a map of where interventions might yield the greatest downstream benefit. Treating depression, optimizing dopaminergic therapy, addressing sexual dysfunction, supporting physical function, and attending to social and role functioning are not competing priorities but complementary leverage points on a shared causal network.

For clinicians, the message is that quality of life in Parkinson’s disease is not a simple readout of motor state. Two patients with comparable disease duration and comparable Unified Parkinson’s Disease Rating Scale scores may inhabit radically different subjective worlds depending on their mood, their intimate relationships, and their ability to sustain valued activities. Routine care that measures only the visible layer of the disease risks systematically underestimating the burden carried by those whose motor symptoms are well controlled but whose emotional, sexual, and social health is not. Screening instruments for depression, anxiety, and sexual function are brief, validated, and inexpensive, making systematic implementation feasible in ordinary outpatient settings.

For researchers, the study demonstrates the value of theory-driven models in a field awash with correlational data. Rather than cataloguing associations, the revised Wilson and Cleary model imposes an ordered structure that can be tested, refined, and compared across conditions and populations. The authors note that longitudinal designs will be needed to confirm the directional pathways suggested here, and that interventions designed with the model in mind, for example psychotherapy or sexual health counseling embedded within neurological care, could provide direct tests of its practical utility. If subsequent trials confirm that improving mental and sexual health yields measurable gains in overall quality of life beyond what motor optimization achieves, the study will have helped shift the center of gravity in Parkinson’s care toward a genuinely holistic medicine, one that treats patients not as motors to be tuned but as whole people whose intimacy, mood, and dignity are inseparable from their health.

Subject of Research: Determinants of health-related quality of life in Parkinson's disease, including sexual and mental health, analyzed with the revised Wilson and Cleary model

Article Title: Sexual, mental, and multidimensional determinants of health-related quality of life in Parkinson's disease using the revised Wilson and Cleary model

Article References: Alenko, A., Sudhakar, M., Chelkeba, L., Fomenko, E., & Keygnaert, I. (2026). Sexual, mental, and multidimensional determinants of health-related quality of life in Parkinson's disease using the revised Wilson and Cleary model. Scientific Reports. https://doi.org/10.1038/s41598-026-71122-z

Image Credits: AI Generated

DOI: 10.1038/s41598-026-71122-z

Keywords: Parkinson's disease, quality of life, Wilson and Cleary model, sexual dysfunction, mental health, depression, non-motor symptoms, health-related quality of life, neurodegenerative disease, patient outcomes, Sexual, mental

These figures deserve careful reflection because they come from a group of people whose primary reason for visiting the hospital was the management of a chronic physical illness rather than a mental health concern. In many low- and middle-income countries, chronic disease clinics are structured around biomedical monitoring: blood pressure readings, blood glucose measurements, prescription refills, and brief consultations. Psychological suffering in such settings can remain invisible unless clinicians actively ask about it. The pattern observed in this study suggests that distress is not an occasional complication of chronic illness but a common companion to it. When more than two thirds of a sample reports extremely severe anxiety, the finding points to a systemic gap in care rather than an isolated clinical problem.

The study also examined how participants coped with illness-related challenges, drawing on the Africultural Coping Systems Inventory, a measure designed to capture coping strategies rooted in African cultural contexts. This choice of instrument is significant. Much of the coping literature has been developed in Western settings and tends to emphasize individual-oriented strategies such as problem-focused planning or cognitive reframing. The Africultural Coping Systems Inventory instead recognizes approaches that are commonly observed in African communities, including collective coping, in which family members, friends, and community networks share the burden of a problem, and cognitive-emotional debriefing, in which individuals work through their feelings by talking them out with others. Measuring these strategies acknowledges that coping is a culturally embedded behavior, not a universal script.

Gender differences emerged in both distress and coping. Female participants reported significantly higher depression, anxiety, and stress scores than male participants, and the effect sizes fell in the moderate-to-large range, indicating differences that are not merely statistical artifacts but meaningful disparities in lived experience. Male participants, by contrast, reported significantly greater use of collective coping and cognitive-emotional debriefing, although the effect sizes here were small. This asymmetry in magnitude is noteworthy. The gender gap in psychological distress was substantial, while the gender gap in coping strategies, though statistically reliable, was more modest. In other words, women in this sample were carrying considerably more emotional weight, and the coping differences detected did not appear large enough on their own to explain that burden fully.

Several lines of reasoning, supported by broader scientific understanding of chronic disease and mental health, help contextualize these findings. Hypertension and diabetes are both conditions that require sustained self-management, and the demands of that management interact with social and economic circumstances. Women in many households assume caregiving responsibilities not only for themselves but for children, partners, and older relatives, which can compress the time and energy available for managing their own health. Dietary recommendations, medication schedules, and clinic visits may be harder to follow when a person is also responsible for feeding a family or working in informal employment without sick leave. Economic vulnerability can also amplify the stress of a condition that requires regular medication, since interruptions in supply or affordability are common in resource-constrained health systems.

The finding of extremely severe anxiety in a majority of participants also invites attention to the biological and psychological interplay between chronic metabolic or cardiovascular disease and emotional states. Anxiety and stress activate physiological pathways that can affect blood pressure and glycemic control, creating a potential feedback loop in which poor mental health worsens the physical condition, which in turn deepens distress. Depression is similarly consequential: it is associated with reduced medication adherence, poorer dietary self-care, and less engagement with follow-up care, all of which can compromise long-term outcomes in hypertension and diabetes. Recognizing this bidirectional relationship strengthens the argument, made by the study’s authors, that routine mental health screening should be embedded within chronic disease clinics rather than treated as a separate service that patients must seek out on their own.

Screening, however, is only a first step. Identification of distress must be linked to accessible psychosocial support, and the study’s findings about coping strategies offer guidance on what such support should look like. Because men in the sample leaned on collective coping and cognitive-emotional debriefing, interventions that mobilize family and community structures may resonate more effectively than purely individual approaches. Support groups organized through existing chronic disease clinics, peer-led discussion sessions, and involvement of household members in counseling could build on strategies that patients already find natural. Culturally relevant care of this kind respects the social fabric through which many Ghanaians navigate illness, rather than importing models that assume an isolated, self-reliant patient.

The higher distress reported by women suggests that gender-sensitive care must go beyond identical treatment for all. It requires attention to the specific pressures women face, which may include economic dependence, caregiving overload, and, in some contexts, limited autonomy in health decisions. Health workers could be trained to ask about these circumstances during routine visits, and referral pathways to counseling or social services could be established within the hospitals where patients already receive care. Task-shifting approaches, in which nurses or trained lay counselors deliver basic psychological interventions, have been explored in various low-resource settings and may offer a practical route to expanding mental health support without requiring large numbers of specialist psychiatrists or psychologists.

The study’s methodology also merits consideration when interpreting its results. As a cross-sectional investigation, it captured a single moment in time for each participant, which means it can document associations between gender, coping, and distress but cannot establish causal direction. It remains possible, for example, that higher distress shapes how people cope rather than the reverse, or that both are influenced by unmeasured factors such as disease duration, severity, income, or social support quality. The reliance on self-report measures introduces the possibility of response bias, and the recruitment of patients from two public hospitals in one region means the findings may not generalize to people managing chronic conditions in private care, in rural communities distant from hospitals, or in other countries. DASS-21 is a screening tool that categorizes symptom severity but is not itself a diagnostic instrument, so the reported percentages reflect symptom burden rather than clinical diagnoses of depressive or anxiety disorders.

Even with these caveats, the scale of the distress documented is difficult to dismiss. The analytic approach, using descriptive statistics and independent t-tests conducted in Jamovi statistical software, was straightforward and transparent, and the moderation of claims about coping differences through small effect sizes reflects a careful reading of the data. The open access publication of the work, carried in Discover Social Science and Health, makes the evidence available to practitioners, policymakers, and researchers in Ghana and beyond, which is particularly valuable for a topic that has received limited attention in resource-constrained settings.

For clinicians, the most immediate implication is the value of asking. A brief, validated screening question about mood or worry during a routine hypertension or diabetes visit costs little and can uncover suffering that patients may not volunteer. For health system planners, the findings argue for integrating mental health services into chronic disease care, a model sometimes described as collaborative or integrated care, so that psychological support becomes a routine component of managing conditions that patients will live with for decades. For communities and families, the findings highlight the role that collective coping already plays and the potential to strengthen it deliberately.

For researchers, the study opens several avenues. Longitudinal designs could clarify how coping strategies and distress influence each other over the course of chronic illness, and how clinical outcomes such as blood pressure control or glycemic stability relate to mental health over time. Qualitative work could illuminate what severe anxiety feels like for a patient managing diabetes in a context of medication shortages, or how women experience the competing demands of illness and family responsibility. Intervention studies could test whether culturally grounded psychosocial support reduces distress and improves self-management.

Ultimately, the study underscores that chronic medical conditions and mental health are inseparable dimensions of the same human experience. The 457 patients who shared their experiences in two hospitals in Ghana’s Central Region reveal a population carrying a heavy and unevenly distributed psychological burden. Women bear more of the distress; men, on average, draw somewhat more on collective and debriefing strategies. Neither pattern can be addressed by biomedical care alone. A health response that treats the blood pressure reading and the glucose value as the whole story will miss the anxiety, depression, and stress documented here. A response that includes routine screening, gender-sensitive support, and culturally relevant psychosocial care would align chronic disease treatment with the full reality of patients’ lives.

The authors’ conclusion, that mental health screening and gender-sensitive, culturally relevant psychosocial support should be considered within chronic disease clinics, is a practical and evidence-based recommendation. Its implementation would require training, resources, and coordination, but the alternative is a system in which the majority of patients with chronic illness experience extreme anxiety without anyone asking about it. This study provides the local evidence needed to begin changing that.

Subject of Research: Gender differences in coping strategies and mental health outcomes among people living with chronic medical conditions

Article Title: Gender differences in coping strategies and mental health outcomes among people living with chronic medical conditions

Article References: Ninnoni, J. P. K., Commey, I. T., Harmah, E. B., Amoadu, M., & Opoku-Danso, R. (2026). Gender differences in coping strategies and mental health outcomes among people living with chronic medical conditions. Discover Social Science and Health. https://doi.org/10.1007/s44155-026-00479-3

Image Credits: AI Generated

DOI: 10.1007/s44155-026-00479-3

Keywords: Gender, differences, coping, strategies, mental, health, outcomes, people, living, chronic, medical, conditions