The research team, led by Rehab Alhazmi of King Saud bin Abdulaziz University for Health Sciences, together with colleagues affiliated with King Abdullah International Medical Research Center and the Ministry of the National Guard — Health Affairs, set out to answer a deceptively simple question: what does resilience actually look like in a Saudi hospital population, and which kinds of social support travel with it? Most earlier work on this topic has treated resilience as a continuous score — a number from a questionnaire that rises and falls across a population. That approach has value, but it can obscure something important: the possibility that people cluster into qualitatively different groups rather than spreading evenly along a line. A person-centered analysis asks whether the data themselves reveal natural groupings, and if so, who ends up in each one.

To do this, the researchers used a technique called latent profile analysis, or LPA. In essence, LPA assumes that an observed pattern of questionnaire responses is generated by an unobserved — latent — categorical variable: membership in one of several hidden groups. The method fits a series of statistical models, each proposing a different number of profiles, and evaluates which model best reproduces the actual distribution of responses. In this study, the winning model contained three profiles of psychological resilience: a low-resilience group comprising 11.68 percent of participants, a moderate group at 37.92 percent, and a high-resilience group that accounted for 50.38 percent of the sample. That is a striking distribution for a clinical outpatient population, where one might expect stress and illness to drag resilience downward; instead, the majority of these patients reported high capacity to cope.

The measurement instruments behind those profiles are well established in the resilience literature. Participants completed the Connor-Davidson Resilience Scale, known as the CD-RISC, a 25-item instrument developed to quantify stress-coping capacity across dimensions such as personal competence, tolerance of negative affect, and the ability to accept change and secure relationships. Social support was measured with the MOS Social Support Survey, or MOS-SSS, a tool derived from the Medical Outcomes Study that decomposes support into functional categories: emotional support — having someone to talk to about problems; informational support — having someone who can offer advice or guidance; affectionate support — having someone who shows love and affection; tangible support, meaning practical help such as assistance with daily tasks; and positive social interaction. This decomposition matters, because the study’s most actionable finding is that not all forms of support are equally tied to resilience.

The cross-sectional design recruited participants through convenience sampling in the outpatient clinics of a general hospital in Riyadh, with all participants providing informed consent under protocols approved by the Institutional Review Board of King Abdullah International Medical Research Center and conducted according to the principles of the Declaration of Helsinki. Once the three resilience profiles were established, the team ran univariate comparisons using chi-square tests for categorical variables and analysis of variance, or ANOVA, for continuous ones, to see how the profiles differed across sociodemographic characteristics and support dimensions. They then turned to multinomial logistic regression, a model that estimates the odds of belonging to one profile rather than another as a function of multiple predictors simultaneously, to isolate which factors genuinely shape profile membership.

Three sociodemographic factors emerged as associated with resilience profile: education, age, and the presence of psychiatric illness. The pattern points toward vulnerability concentrating among people with lower educational attainment, younger adults, and those living with mental illness. Each of these associations makes biological and social sense. Education is a well-documented correlate of cognitive resources — the vocabulary, problem-solving habits, and health literacy that people deploy when interpreting and managing stressors. Age, meanwhile, intersects with the accumulated experience of having survived previous adversity; younger adults simply have fewer rehearsals of coping behind them. And comorbid psychiatric illness is both a consumer and a casualty of resilience: conditions such as depression and anxiety erode the very cognitive and emotional machinery that resilience depends on, while low resilience can deepen the psychiatric burden, creating a feedback loop that clinics are well positioned to interrupt.

The social support findings add a second, equally practical layer. Affectionate support, along with emotional and informational support, were each associated with psychological resilience profiles. Notably, tangible support — the practical, hands-on kind — was not highlighted in the same way. That distinction carries real weight for anyone designing an intervention. It suggests that resilience is nourished less by what people do for you and more by what people make you feel and know: affection that affirms your worth, emotional presence that lets you process fear, and information that converts uncertainty into a plan. In a hospital waiting room, this reframes the question a nurse or physician might ask. Beyond arranging transport or medication assistance, the data hint that asking whether a patient has someone to talk to, someone who listens, and someone who can help them understand what is happening may identify the patients whose coping capacity is most at risk.

The authors frame the clinical implication in explicitly targeted terms: healthcare practitioners may need to pay particular attention to individuals with lower education, younger age, mental illness, and a lack of social support, and to design interventional strategies that strengthen psychological resilience specifically for these vulnerable groups, enhancing their ability to manage health-related stressors and thereby improve well-being. In practice, that could mean resilience-building programs embedded in outpatient care — structured training in cognitive reframing, stress management, and help-seeking — paired with deliberate efforts to connect isolated patients to family, peer, or community networks. Because the study is cross-sectional, it cannot prove that low support causes low resilience; it is equally plausible that people with weaker coping reach out less and therefore receive less. But the co-occurrence of the two, in a large and culturally specific sample, is precisely the kind of signal that justifies testing support-enhancing interventions prospectively.

The cultural context is not incidental. The authors note that previous studies support a link between resilience and social support, but that variations in cultural contexts and populations limit how well those findings generalize to Saudi Arabia. Family structure, religious practice, and community norms in the Gulf shape both how support is given and how coping is expressed, and resilience research imported wholesale from Western samples may miss those dynamics entirely. By running a person-centered analysis on a Saudi outpatient population, the study contributes locally grounded evidence — and its headline distribution, with half the sample in the high-resilience profile, offers a counterpoint to assumptions that clinical populations are uniformly fragile. At the same time, the 11.68 percent in the low-resilience group represent a substantial minority whose needs could be invisible under an average-based analysis, which is exactly the blind spot LPA is designed to correct.

There are, of course, limits worth keeping in view. Convenience sampling in a single hospital’s outpatient clinics means the sample may not represent the broader Saudi population, and the reliance on self-report instruments means resilience and support are measured as people perceive them, not as an external observer would. The study also had no external funding, and the authors declare no competing interests. Yet the methodological contribution stands on its own: by identifying three distinct resilience profiles and mapping them onto education, age, psychiatric status, and specific flavors of social support, the Riyadh team has turned an abstract psychological construct into a triage-ready framework. The next step — testing whether bolstering affectionate, emotional, and informational support actually moves patients between profiles — could transform a statistical insight into a clinical routine, one conversation at a time.

Subject of Research: Psychological resilience profiles and their association with social support among adult hospital outpatients in Saudi Arabia

Article Title: Psychological resilience and social support among adult outpatients visiting a general hospital in Saudi Arabia

Article References: Alhazmi, R., Dawood, E., Shereda, H. M. A., Alotaibi, A., Alqarni, A., Alharbi, R., Alanazi, G., & Almutiri, L. (2026). Psychological resilience and social support among adult outpatients visiting a general hospital in Saudi Arabia. Discover Mental Health. https://doi.org/10.1007/s44192-026-00613-2

Image Credits: AI Generated

DOI: 10.1007/s44192-026-00613-2

Keywords: psychological resilience, social support, latent profile analysis, outpatients, Saudi Arabia, Connor-Davidson Resilience Scale, MOS Social Support Survey, mental health, cross-sectional study, psychiatric illness, healthcare, Riyadh

The study’s central innovation is a new interview format called the Multi-Topic Emotion Interview, or MTEI. Rather than asking patients to complete a resilience scale, the researchers guided them through conversations spanning multiple emotional topics, recording both audio and momentary self-assessments during the session. Between December 2024 and November 2025, the team collected multimodal data from 248 patients diagnosed with major depressive disorder and 101 healthy controls. To test whether their models would hold up beyond the original cohort, they also ran a four-week longitudinal follow-up with 106 participants, creating a temporal validation set that mimicked the real-world challenge of applying a model to new patients weeks after it was built. The study was retrospectively registered with the Chinese Clinical Trial Registry under identifier ChiCTR2500100966 on April 17, 2025, and approved by the ethics committee of Beijing Anding Hospital.

At the heart of the analysis sits an expert-guided large language model, a design choice that distinguishes this work from most speech-based mental health studies. Instead of letting a general-purpose language model freely interpret transcripts, the researchers steered it with expert knowledge to extract two families of features. The first is a Semantic Resilience Index, or SRI, a quantitative score capturing how resilient a patient’s language sounds in terms of coping strategies and adaptive thinking. The second is a three-dimensional emotional profile known in the literature as VAD, standing for valence, arousal, and dominance, which characterizes the emotional tone of what a patient says along dimensions of pleasantness, intensity, and sense of control. These language-derived features were then combined with Self-Assessment Manikin ratings, a validated pictorial instrument in which patients report their momentary emotional state during the interview itself.

To turn these features into a continuous resilience score, the team employed a Voting Regressor ensemble, a machine learning technique that combines the predictions of multiple regression models to produce a more stable estimate. The target variable was the Connor-Davidson Resilience Scale, or CD-RISC, the standard clinical questionnaire for measuring resilience. The best-performing multimodal fusion model, which integrated the semantic resilience index, the VAD emotional features, and the SAM state assessments, achieved an R-squared of 0.555 and a Concordance Correlation Coefficient of 0.705. In practical terms, the model explained more than half of the variance in questionnaire-measured resilience, and its predictions agreed closely with the actual scores patients reported. Those are respectable figures for a behavioral biomarker, a field where effect sizes are often far weaker.

What makes the result scientifically interesting is how well the model traveled through time. When evaluated on the four-week follow-up data from 106 participants, performance barely moved: R-squared of 0.557 and a Concordance Correlation Coefficient of 0.701. In machine learning for medicine, models frequently degrade when applied to data collected later, a phenomenon driven by drift in recording conditions, patient populations, or clinical workflows. The near-identical metrics here suggest that the speech-derived resilience signal is stable rather than an artifact of a particular recording session. For a digital biomarker intended to be used repeatedly across the course of treatment, temporal stability is arguably as important as raw accuracy, and this study provides unusually direct evidence for it.

The researchers then asked a harder question: was the model actually measuring resilience, or was it merely detecting how depressed someone was? Because depression severity and resilience are related, a model could score well on resilience prediction while really just tracking mood. To disentangle the two, the team performed residual analysis, statistically removing the contribution of depression severity and examining what the model still captured. The answer was that the model explained 9.6 percent of the variance in pure resilience residuals, meaning it genuinely decoded resilience-specific information that exists independently of current depressive symptoms. This is the study’s most consequential finding, because it demonstrates that a speech-based system can identify a patient’s coping capacity even when that patient is in the depths of a depressive episode, when self-report instruments are least reliable.

Interpretability, often the Achilles heel of large language model applications in medicine, received explicit attention through SHAP analysis, a technique that quantifies how much each feature contributes to a model’s predictions. The analysis revealed that the most critical predictors were three LLM-derived constructs: agency and control, reflecting the degree to which patients describe themselves as capable of influencing outcomes; cognitive nuance, capturing the sophistication and flexibility of their thinking about difficult situations; and contextual emotional dominance, a measure of perceived control within emotionally charged content. These are precisely the psychological mechanisms that resilience theory says should matter, which lends the model a degree of face validity that purely data-driven features often lack. A clinician looking at the model’s output would not receive an opaque number but a profile grounded in recognizable cognitive dimensions.

Equally notable is what failed. Traditional acoustic features, the pitch, energy, and rhythm measures that have dominated speech-based depression research for decades, did not provide independent incremental validity during late fusion, the stage at which different feature streams are combined. In other words, once the model had access to the deep semantic features extracted by the expert-guided language model, the raw sound of the voice added essentially nothing. This challenges a common assumption in the digital psychiatry field that paralinguistic vocal cues are the primary carriers of diagnostic information. The finding suggests that for resilience specifically, what patients say and how they frame their experiences may matter more than how their voices sound, a conclusion that could redirect research funding and design choices across the field.

The clinical implications are considerable. A scalable, objective resilience measure could change how treatment is personalized in psychiatry. Two patients with identical symptom scores may have very different recovery trajectories depending on their coping resources, and today those differences are largely invisible to standard assessments. A speech-based biomarker could be administered during routine visits, requiring only a short interview and no additional hardware beyond a microphone, and could track whether an intervention is actually strengthening resilience rather than just suppressing symptoms. The authors suggest that the identified predictors, particularly agency and control and cognitive nuance, point to actionable targets for personalized interventions, since these are constructs that psychotherapies such as cognitive behavioral therapy explicitly aim to modify.

Cautions remain before such a tool reaches the clinic. The cohort was recruited at a single Chinese psychiatric hospital, and cross-cultural validation will be essential, since both language use and the expression of coping differ across populations. The follow-up period of four weeks is short relative to the relapsing course of major depressive disorder, and the study measured resilience against a self-report gold standard, albeit one that the model demonstrably exceeded in objectivity. The expert-guided design also raises questions about how sensitive the system is to the specific prompting framework used. Still, the combination of a large multimodal cohort, temporal validation, residual analysis, and mechanistic interpretability makes this one of the more rigorous demonstrations to date that language, parsed intelligently, can carry clinically meaningful psychiatric signal. As large language models continue to mature, the quiet details of how patients talk about their lives may become one of psychiatry’s most informative vital signs.

Subject of Research: Speech-based digital biomarkers of psychological resilience in major depressive disorder using expert-guided large language models

Article Title: Interpretable speech biomarkers of psychological resilience in major depressive disorder via expert-guided large language models

Article References: Wang, S., Zhu, X., Li, N., Yang, Q., Liu, Y., Wang, J., Liu, P., Feng, L., & Wang, G. (2026). Interpretable speech biomarkers of psychological resilience in major depressive disorder via expert-guided large language models. BMC Psychiatry. https://doi.org/10.1186/s12888-026-08584-y

Image Credits: AI Generated

DOI: 10.1186/s12888-026-08584-y

Keywords: major depressive disorder, psychological resilience, digital biomarker, large language model, speech analysis, natural language processing, Connor-Davidson Resilience Scale, SHAP analysis, multimodal fusion, psychiatric assessment, semantic resilience index, machine learning

The study recruited 2,378 nursing students from four medical colleges across Hubei Province, collecting data between January 2025 and March 2026. Each participant completed two well-established psychometric instruments. The first was the 25-item Connor-Davidson Resilience Scale, a questionnaire that probes multiple facets of resilience, including personal competence, trust in one’s instincts, tolerance of negative affect, acceptance of change, control and spiritual influences. The second was the 21-item Depression Anxiety Stress Scales, known as DASS-21, which yields separate scores for each of the three emotional states as well as a composite measure of overall distress. The research team received ethics approval from the Institutional Review Board of Hubei University of Medicine, conducted all procedures in accordance with the Declaration of Helsinki, and obtained written informed consent from every participant, with responses collected anonymously and participation entirely voluntary.

The analytical centerpiece of the study was latent profile analysis, a person-centered statistical method that differs fundamentally from the variable-centered approaches most readers will be familiar with. Where a traditional correlation asks how scores relate across a whole sample, latent profile analysis asks whether the sample actually contains hidden subgroups, or profiles, of individuals who answer the resilience items in similar ways. The method fits a series of statistical models, each proposing a different number of profiles, and uses fit indices such as the Akaike information criterion, the Bayesian information criterion and bootstrapped likelihood-ratio tests to decide how many profiles the data genuinely support. A key quality metric is entropy, which ranges up to 1.0 and indicates how cleanly individuals are sorted into their assigned profiles. In this study the three-profile solution achieved an entropy of 0.958, an exceptionally high value that signals near-unambiguous classification.

That three-profile solution revealed a striking asymmetry in the distribution of resilience. The largest group, comprising 52.2 percent of students, showed a lower adversity-adaptation profile, meaning their item-level responses reflected comparatively limited capacity to adapt to hardship. A second group of 40.6 percent occupied an intermediate position, while only 7.2 percent of students belonged to a higher adversity-adaptation profile marked by robust resilience across the measured dimensions. The proportions themselves are a finding worth pausing on: fewer than one in twelve future nurses in the sample displayed the highest resilience pattern, while more than half fell into the lowest. For a profession whose daily work demands sustained psychological endurance, that distribution is a sobering portrait of the pipeline supplying the clinical workforce.

The emotional health consequences of profile membership followed a clean graded pattern. Depression, anxiety, stress and total DASS-21 scores all differed significantly across the three profiles, with all comparisons reaching the p < 0.001 threshold. Students in the lower adversity-adaptation profile reported the highest distress, those in the intermediate profile fell in the middle, and those in the higher profile reported the lowest. When the researchers quantified these differences against the higher profile as the reference group, the lower profile was associated with total DASS-21 scores 18.661 points higher, and the intermediate profile with scores 5.775 points higher. In psychometric terms, an 18-point gap on the DASS-21 total represents a clinically meaningful difference in overall emotional distress, not a statistical curiosity.

To test whether resilience profiles carried information beyond basic demographics, the team built hierarchical regression models. A first model incorporating six demographic, family and relational covariates explained 8.7 percent of the variance in total DASS-21 scores. Adding profile membership to that model raised the explained variance to 18.1 percent, an increase of 9.4 percentage points. In other words, knowing a student’s resilience type nearly doubled the predictive power of the model, demonstrating that the profiles capture psychological information that conventional background variables miss. The researchers also employed multinomial logistic regression and the Bolck-Croon-Hagenaars procedure, a technique that corrects for classification uncertainty when relating latent profiles to external outcomes, to characterize which students ended up in which profiles and to confirm the profile-outcome associations.

The profiles were not randomly distributed across the student body. Differences in sociodemographic and relational characteristics distinguished the groups, indicating that family circumstances, interpersonal relationships and demographic factors shape which resilience pattern a student develops. The study’s cross-sectional design means these associations cannot be read as causal chains; it is equally plausible that chronic distress erodes resilience, that low resilience amplifies distress, or that shared upstream factors drive both. What the design does establish, with considerable statistical confidence, is that the two constructs are tightly intertwined and that resilience profiles function as a powerful marker of who is most likely to be suffering.

Why does the person-centered framing matter so much for practice? Traditional screening in nursing schools typically relies on either a single resilience score or direct symptom questionnaires. A single score conceals the architecture of a student’s psychological resources: two students with identical total resilience scores may arrive there through entirely different patterns of strengths and vulnerabilities. By identifying discrete profiles, the latent profile approach gives educators a typology rather than a number. The authors suggest that nursing schools could combine universal support programs, offered to everyone, with direct symptom assessment and mentoring targeted to students’ needs, using profile membership as one signal for allocating attention. The graded relationship between profiles and distress means the intermediate group, which is large and often overlooked because its members are neither the most nor the least distressed, may deserve particular attention.

The study also carries implications for the nursing profession at large. Burnout, emotional exhaustion and attrition among nurses are persistent global problems, and the psychological habits formed during training are widely believed to carry forward into professional practice. If more than half of entering nursing students show the lowest resilience pattern, and if that pattern is associated with markedly elevated depression, anxiety and stress, then the classroom and the clinical placement become critical intervention windows. Resilience, unlike fixed traits, is considered modifiable, and profile-informed support could in principle be designed to strengthen the specific adaptive capacities that a given profile lacks, rather than applying generic wellness programming uniformly.

The authors are careful about the limits of their evidence. Because the data were collected at a single point in time, the direction of the relationship between resilience profiles and emotional symptoms remains undetermined, and the sample, drawn entirely from medical colleges in one Chinese province, may not generalize to nursing students elsewhere. The researchers explicitly call for longitudinal and intervention studies to determine whether profile-informed support actually improves outcomes. Still, the technical achievement is substantial: a near-perfect classification solution, a graded and highly significant association with distress, and a demonstration that resilience profiles add nearly ten percentage points of predictive variance beyond demographics. As mental health screening in professional education moves toward precision approaches, this study offers a template for how hidden psychological subgroups can be surfaced from ordinary questionnaire data, and a reminder that the students caring for tomorrow’s patients need their own care designed with the same sophistication.

Subject of Research: Resilience profiles and their association with depression, anxiety and stress among Chinese nursing students

Article Title: Latent profile analysis of resilience and its relationship with depression-anxiety-stress among Chinese nursing students: a cross-sectional study

Article References: Wang, Y., Zhang, T., Wu, M., Li, X., Xiao, L., & Ke, L. (2026). Latent profile analysis of resilience and its relationship with depression-anxiety-stress among Chinese nursing students: a cross-sectional study. BMC Psychology. https://doi.org/10.1186/s40359-026-05720-x

Image Credits: AI Generated

DOI: 10.1186/s40359-026-05720-x

Keywords: resilience, nursing students, latent profile analysis, depression, anxiety, stress, mental health, DASS-21, Connor-Davidson Resilience Scale, cross-sectional study, China, psychological assessment

The research team, led by investigators affiliated with Beirut Arab University, recruited 299 adults with confirmed heart failure from outpatient clinics and primary healthcare services across all major regions of Lebanon between January 2024 and October 2025. Participants were Arabic-speaking adults aged 30 to 80 years with either reduced ejection fraction of 40 percent or less, or symptomatic preserved ejection fraction confirmed by echocardiography, diagnosed for at least three months according to the Framingham criteria. Of 475 people initially screened, some were excluded because their understanding of the term heart failure was distorted, a striking detail in itself: many patients who had undergone open-heart surgery misinterpreted the diagnosis, underlining how much work remains in patient education. After removing cases with missing data, 299 participants formed the analytical sample.

The burden carried by this group was considerable. More than 90 percent had hypertension, roughly 60 percent had diabetes, and the average Charlson Comorbidity Index was 5.58, indicating substantial multimorbidity. Depression had been formally diagnosed in 35.5 percent of participants. The mean left ventricular ejection fraction was 37.52 percent, nearly half the sample was in New York Heart Association functional class III, and participants had lived with heart failure for an average of 5.3 years. Most were unemployed, nearly half reported a monthly household income between 200 and 500 US dollars, and almost all relied on a family caregiver, most often a spouse or an adult child. These socioeconomic realities matter, because in Lebanon out-of-pocket medication costs and repeated hospital admissions place enormous financial pressure on households already battered by currency collapse and inflation.

To measure the study’s central construct, the researchers used the Connor-Davidson Resilience Scale, a 25-item instrument covering domains such as personal competence, trust in one’s instincts, positive acceptance of change, control, and spiritual influences, scored from 0 to 100. In this Lebanese sample the scale showed exceptional internal consistency, with a Cronbach’s alpha of 0.965. Quality of life was assessed with the Arabic-validated Minnesota Living with Heart Failure Questionnaire, depressive symptoms with the Patient Health Questionnaire-9, and self-care with the Revised Self-Care Heart Failure Index version 7.2, which captures self-care maintenance, symptom monitoring, and self-care management as distinct subscales. All instruments had been previously translated and psychometrically evaluated for the Lebanese population, and reliability coefficients in the current study ranged from 0.86 to 0.965.

The headline result was a picture of a population split almost exactly in two. The mean resilience score was 58.41, just above the developer’s suggested cut-off of 55, and 51.2 percent of participants were classified as resilient while 48.8 percent were not. Depressive symptoms averaged 13.76 on the PHQ-9, corresponding to moderate depression, with more than half of the sample falling into the moderate to severe categories. Self-care maintenance and monitoring scores were low at 62.68 and 64.71, below the conventional adequacy threshold of 70, but self-care management scored 78.24, the only subscale to surpass the cut-off. Quality of life, at 56.07 on the Minnesota questionnaire, reflected moderate impairment. Statistically, resilience correlated negatively with depression and positively with all three self-care subscales, with the strongest correlation, r = 0.540, linking resilience to self-care maintenance.

The multivariable regression model identified four independent predictors of resilience, explaining 36.1 percent of the variance in resilience scores. Higher self-care maintenance and higher self-care management each independently predicted greater resilience, with unstandardized coefficients of 0.248 and 0.282 respectively. Male participants scored higher than female participants after adjustment, a gender difference of just over four points on the CD-RISC. Perhaps most intriguingly, participants taking loop diuretics showed resilience scores more than eight points higher than those not taking this class of medication, a finding that likely reflects the association between being on guideline-directed therapy and being engaged with care rather than a pharmacological effect on psychological coping. Bivariate analyses added further texture: resilient patients were more likely to have received influenza and COVID-19 vaccinations, to be current with medical follow-up, to have higher education, and to be cared for by a spouse rather than a child.

One of the most provocative findings is the negative, though statistically non-significant, correlation between resilience and quality of life, which runs counter to virtually all of the published literature, including studies from Egypt, Iraq, and general populations worldwide. The authors argue that no prior research has examined a population exposed to the compounded burden of simultaneous health, political, social, and economic crises as Lebanon has experienced since 2019, encompassing currency devaluation, hyperinflation, the Beirut Port explosion, and continuing regional instability. In such a context, the usual relationship between adaptation and wellbeing may be distorted: patients who have been forced into self-reliance by a failing system may report functional resilience even as their perceived quality of life erodes. The team also notes that quality-of-life scores among Lebanese heart failure patients nearly doubled between 2018 and 2022 before stabilising, a trajectory that mirrors the country’s crisis timeline.

The self-care management result tells a parallel story. In earlier Lebanese studies, self-care management was consistently the weakest subscale, with mean scores around 47. In the present sample it exceeded 78, suggesting a growing culture of self-reliance in disease management driven by necessity, as patients navigate medication shortages, costly follow-up visits, and disrupted continuity of care. This shift occurred despite previously documented deficiencies in discharge education. For nursing science, the implication is that resilience is not a fixed trait but a modifiable domain that can be assessed, monitored, and supported through education, counselling, family-inclusive care planning, and community-based follow-up, particularly in resource-constrained settings where nurses and advanced practice nurses are often the most accessible point of contact.

The authors are careful to acknowledge the limitations of a cross-sectional design, which cannot establish causal direction between resilience and self-care, along with the possibility of social desirability bias in self-reported questionnaires and the constraints of convenience sampling. Even so, the study stands as the first to document resilience and its predictors among heart failure patients living through sustained national collapse, and its patterns may generalise to Lebanese diaspora communities and to other populations enduring protracted instability. The practical message for clinicians is concrete: screening for low resilience, reinforcing self-care maintenance behaviours, supporting female patients who carry lower resilience scores, ensuring guideline-directed medication including diuretic therapy, leveraging spousal caregivers, and promoting vaccination and regular follow-up may together form the scaffolding that keeps chronically ill patients upright when the systems meant to support them cannot.

Subject of Research: Predictors of psychological resilience among patients living with heart failure during prolonged sociopolitical and economic crises in Lebanon

Article Title: Predictors of Resilience Among Patients Living With Heart Failure Amid Escalating Sociopolitical Challenges: A Cross‐Sectional Approach

Article References: Deek, H., Massouh, A. R., & Shatila, W. (2026). Predictors of Resilience Among Patients Living With Heart Failure Amid Escalating Sociopolitical Challenges: A Cross‐Sectional Approach. Nursing Open, 13(9), Article e70816. https://doi.org/10.1002/nop2.70816

Image Credits: AI Generated

DOI: 10.1002/nop2.70816

Keywords: heart failure, resilience, self-care, Lebanon, depression, quality of life, nursing, cross-sectional study, Connor-Davidson Resilience Scale, chronic illness, sociopolitical crisis, cardiology