psychological resilience research paper

Psychological resilience: an update on definitions, a critical appraisal, and research recommendations

Affiliations.

• 1 Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
• 2 Institute of Child Development, University of Minnesota, Minneapolis, USA.
• 3 Department of Social and Behavioral Sciences, Lee Kum Sheung Center for Health and Happiness, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
• 4 Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa.
• 5 Harvard Medical School, Boston, MA, USA.
• 6 Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
• 7 Department of Sociology, Harvard University, Cambridge, MA, USA.
• PMID: 33244362
• PMCID: PMC7678676
• DOI: 10.1080/20008198.2020.1822064

Abstract in English, Spanish, Chinese

Background: The ability to resist adverse outcomes, or demonstrate resilience after exposure to trauma is a thriving field of study. Yet ongoing debate persists regarding definitions of resilience, generalizability of the extant literature, neurobiological correlates, and a consensus research agenda. Objectives: To address these pressing questions, Drs. Christy Denckla and Karestan Koenen (co-chairs) convened a multidisciplinary panel including Drs. Dante Cicchetti, Laura Kubzansky, Soraya Seedat, Martin Teicher, and David Williams at the 2019 annual meeting of the International Society for Traumatic Stress Studies (ISTSS). Questions included (1) how have definitions of resilience evolved, (2) what are the best approaches to capture the complexity of resilience processes, and (3) what are the most important areas for future research? Methods: The proceedings of this panel are summarized in this report, and prominent themes are synthesized and integrated. Results: While different definitions emerged, all shared a focus on conceptualizing resilience at multiple levels, from the biological to the social structural level, a focus on the dynamic nature of resilience, and a move away from conceptualizing resilience as only an individual trait. Critical areas for future research included 1) focused efforts to improve assessment that has international and cross-cultural validity, 2) developing within-study designs that employ more intensive phenotyping strategies, 3) examining outcomes across multiple levels and domains, and 4) integrating conceptualizations of resilience from the individual-level to the larger social context at the population health level. Conclusion: Increasingly sophisticated and nuanced conceptual frameworks, coupled with research leveraging advances in genetics, molecular biology, increased computational capacity, and larger, more diverse datasets suggest that the next decade of research could bring significant breakthroughs.

Antecedentes : La capacidad de los sujetos para resistirse a resultados adversos – o demostrar resiliencia – luego de la exposición al trauma es un campo de estudios creciente. Sin embargo, persiste el debate en las definiciones de resiliencia, en cuáles son sus sustratos neurobiológicos, en qué medida los hallazgos de la literatura existente pueden ser generalizados, y en la dirección que debe tomar la investigación futura. Objetivos : Para abordar estas preguntas urgentes los doctores Christy Denckla y Karestan Koen (copresidentes) convocaron un panel multidisciplinario que incluyó a los doctores Dante Cicchetti, Laura Kubzansky, Soraya Seedat, Martin Teicher y David Williams, en el encuentro anual de la Sociedad Internacional para los Estudios del Estrés Traumático (ISTSS por sus siglas en inglés) del 2019. Las preguntas incluyeron (1) cómo han evolucionado las definiciones de resiliencia, (2) cuáles son los mejores enfoques para capturar la complejidad de los procesos de resiliencia, y (3) ¿cuáles son las áreas más importantes para la investigación futura? Métodos : Las actas de este panel se resumen en este informe, y los temas destacados se sintetizan e integran. Resultados : Si bien surgieron diferentes definiciones, todas compartían el enfoque de conceptualizar la resiliencia en múltiples niveles, desde el nivel biológico hasta el nivel social estructural, el enfoque de la naturaleza dinámica de la resiliencia, y el dejar de conceptualizar la resiliencia como un rasgo individual. Las áreas álgidas de investigación a futuro incluyen 1) esfuerzos enfocados en mejorar la evaluación de la resiliencia con validación internacional e intercultural, 2) desarrollar diseños de estudio que utilicen estrategias de fenotipificación más intensivas, 3) evaluar los resultados a lo largo de múltiples niveles y dominios, y 4) integrar conceptualizaciones de la resiliencia desde el nivel individual hacia el contexto social en niveles de salud poblacionales. Conclusión : Los marcos conceptuales cada vez más sofisticados y matizados, junto con la investigación que aprovecha los avances en genética, biología molecular, mayor capacidad computacional y conjuntos de datos más grandes y diversos, sugieren que la próxima década de investigación podría traer importantes avances.

背景: 在暴露于创伤后，抵抗不良后果或表现出韧性的能力是一个蓬勃发展的研究领域。然而，关于韧性的定义、现有文献的概括性、与神经生物学的相关性以及共识研究议程仍存在持续争议。 目的： 为解决这些紧迫问题，在2019年国际创伤应激研究学会（ISTSS）年会上，Christy Denckla和Karestan Koenen博士（联合主席）召集了包括Dante Cicchetti、Laura Kubzansky、Soraya Seedat、Martin Teicher和David Williams博士在内的多学科专家组。问题包括：（1）韧性的定义如何演变; （2）捕捉韧性复杂过程的最佳方法是什么; （3）未来研究最重要的领域是什么？ 方法: 本报告总结了该专家组的会议记录，并对主要主题进行了综合整理。 结果: 尽管出现了不同的韧性定义，但所有定义都关注从生物结构到社会结构对韧性进行多水平的概念化，都关注韧性的动态性质，以及都不再仅仅将韧性概念化为个体特质。未来研究的关键领域包括：1）集中精力改进具有国际和跨文化有效性的评估; 2）开发采用更深入的表型分析策略的研究设计，3）考查跨多个水平和领域的结果，以及4）整合从个体水平到更大的人群健康水平的社会背景的韧性概念。 结论: 越来越复杂和细致的概念框架，结合促进了遗传学、分子生物学、增强的计算能力以及更大、更多样化数据进步的研究，表明未来十年的研究可能会带来重大突破。 关键词： 韧性; 应激; 创伤; 创伤后应激障碍.

Keywords: Resilience; post-traumatic stress disorder; stress; trauma.

© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Grants and funding

• K23 MH117278/MH/NIMH NIH HHS/United States
• UL1 TR001442/TR/NCATS NIH HHS/United States
• P30 AI036214/AI/NIAID NIH HHS/United States
• R01 DA017846/DA/NIDA NIH HHS/United States
• R01 HD079484/HD/NICHD NIH HHS/United States

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• Published: 27 November 2021

Psychological and biological resilience modulates the effects of stress on epigenetic aging

• Zachary M. Harvanek   ORCID: orcid.org/0000-0003-3702-1051 1 ,
• Nia Fogelman 2 ,
• Ke Xu   ORCID: orcid.org/0000-0002-6472-7052 1 , 3 &
• Rajita Sinha   ORCID: orcid.org/0000-0003-3012-4349 1 , 2 , 4 , 5  

Translational Psychiatry volume  11 , Article number:  601 ( 2021 ) Cite this article

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Our society is experiencing more stress than ever before, leading to both negative psychiatric and physical outcomes. Chronic stress is linked to negative long-term health consequences, raising the possibility that stress is related to accelerated aging. In this study, we examine whether resilience factors affect stress-associated biological age acceleration. Recently developed “epigenetic clocks” such as GrimAge have shown utility in predicting biological age and mortality. Here, we assessed the impact of cumulative stress, stress physiology, and resilience on accelerated aging in a community sample ( N  = 444). Cumulative stress was associated with accelerated GrimAge ( P  = 0.0388) and stress-related physiologic measures of adrenal sensitivity (Cortisol/ACTH ratio) and insulin resistance (HOMA). After controlling for demographic and behavioral factors, HOMA correlated with accelerated GrimAge ( P  = 0.0186). Remarkably, psychological resilience factors of emotion regulation and self-control moderated these relationships. Emotion regulation moderated the association between stress and aging ( P  = 8.82e−4) such that with worse emotion regulation, there was greater stress-related age acceleration, while stronger emotion regulation prevented any significant effect of stress on GrimAge. Self-control moderated the relationship between stress and insulin resistance ( P  = 0.00732), with high self-control blunting this relationship. In the final model, in those with poor emotion regulation, cumulative stress continued to predict additional GrimAge Acceleration even while accounting for demographic, physiologic, and behavioral covariates. These results demonstrate that cumulative stress is associated with epigenetic aging in a healthy population, and these associations are modified by biobehavioral resilience factors.

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Introduction.

Cumulative stress can have adverse psychiatric and physical effects, increasing risk for cardiometabolic diseases, mood disorders, post-traumatic stress disorder and addiction [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. There are several potential psychological and biological mechanisms through which these effects may occur. For example, stress may reduce psychological resilience measures such as emotion regulation and self-control that are known to protect against psychiatric and physical health outcomes [ 1 , 12 , 13 , 14 ]. Notably, emotional stress exposure decreases cognitive and emotion regulation abilities [ 15 , 16 , 17 , 18 ], and this effect may be modulated by cortisol [ 15 ]. Furthermore, stress decreases self-control abilities [ 19 , 20 , 21 ] and impacts the likelihood of individuals engaging in healthy behaviors such as exercise or maintaining a healthy diet, and is associated with unhealthy behaviors such as smoking, alcohol, and drug use [ 22 , 23 , 24 , 25 ]. Recent evidence also suggests that stress effects on metabolic health may be affected by BMI-related changes in insulin resistance and other gut hormones [ 26 , 27 ]. Indeed, stress’s effects on physiology resulting in alterations in neuro-hormonal signaling pathways as well as increased inflammation are well documented [ 26 , 28 , 29 , 30 ]. Both stress and these physiologic changes may increase the risk of multiple physical and psychiatric illnesses, which in turn increase morbidity and mortality risk. This has often been described as an increased allostatic load, and notably many of these processes, such as metabolic and cardiovascular dysfunction, have been associated with human aging [ 31 ]. For example, insulin signaling might be linked to aging and aging-related diseases in humans [ 32 ], with recent data on metformin (a treatment for insulin resistance) suggesting it may be useful as an anti-aging drug [ 33 ].

There is growing evidence that cumulative stress may adversely impact health via accelerating the cellular aging process. For example, stress shortens telomere length and alters telomerase activity, and this interaction is modified by behavioral and psychological resilience factors [ 34 , 35 , 36 , 37 ]. However, recent studies have demonstrated mixed results on whether characteristics that contribute to resilience improve or worsen the impact of stress on health [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ]. These data suggest that resiliency factors may modulate the relationship between chronic stress and aging, but to our knowledge this has not been tested in a healthy community sample. While there are many aspects of resilience, including cultural/societal, environmental, and personal which can decrease the negative consequences of stressors on individuals, herein we will focus on personal-level, psychological skills, including self-control and emotion regulation.

Recently developed DNA methylation-based epigenetic “clocks” appear to provide a more accurate measure of biological age than telomere length [ 48 , 49 , 50 , 51 ]. These clocks are built from a set of DNA methylation markers that correlate with chronologic age and serve as molecular estimators of biological age in cells, tissues, and individuals [ 52 ]. Epigenetic clocks have a significantly higher predictive value than previously used measures such as telomere length for frailty, [ 53 ] mortality risk [ 54 , 55 ], hazard ratios [ 56 ], and chronologic age [ 57 ]. The development of these biological aging markers promises to not only aid in identifying individuals at higher risk for aging-related illnesses, but potentially also developing interventions to prevent accelerated aging.

Previous studies (reviewed by Palma-Gudiel et al [ 58 ]) have utilized epigenetic clocks to demonstrate associations between trauma, early life adversity, or low socioeconomic status and accelerated epigenetic aging. Studies have often been focused upon selected populations, such military veterans [ 45 ], individuals with significant trauma histories [ 59 ], or specific cohorts at higher risk [ 60 , 61 , 62 ]. Notably, these studies did not exclude, and often explicitly included, individuals with significant mental and physical illnesses, including PTSD, MDD, and other disabilities [ 59 , 63 ]. These studies also primarily utilized epigenetic clocks trained upon chronologic age. However, a recently developed epigenetic clock, GrimAge, was trained using biomarkers of mortality and indicators of health, and has superior performance in predicting health outcomes when compared with other epigenetic clocks [ 51 , 64 ].

We utilized GrimAge Acceleration (“GAA”, defined as the residual of the regression of GrimAge to chronologic age, with a positive number indicating biological age greater than chronologic age) to conduct a cross-sectional study to answer three questions. First, is cumulative stress related to epigenetic markers of biological aging in a healthy young-to-middle-aged community population? Second, if stress is associated with epigenetic aging, does stress-related physiology contribute to stress-associated biological aging? And finally, how do psychological factors that contribute to resilience modulate these relationships? Based on previous research, we hypothesized that cumulative stress will be positively associated with GrimAge Acceleration (GAA), that stress effects on GrimAge will be related to changes in the hypothalamic-pituitary-adrenal axis (HPA) and insulin sensitivity, and that strong emotion regulation as measured by the Difficulties in Emotion Regulation Scale (DERS, [ 65 ]) and high self-control as measured by the Self Control Scale-Brief (SCS-B, [ 66 ]) will moderate the relationships between stress, physiology, and accelerated aging (See Fig. 1 for a model summarizing our hypotheses).

We hypothesize that stress is positively associated with accelerated biological aging, which we measure via GrimAge Acceleration (GAA), and that this relationship will be mediated by stress-related physiologic changes such as insulin and HPA signaling. We also hypothesize that strong psychological resilience factors will be protective against the negative consequences of stress on aging. Note that these relationships are predictive, not causative, as this study is cross-sectional and thus directionality of relationships cannot be conclusively examined.

Materials and methods

Cohort recruitment.

The participant cohort included 444 community adults between the ages of 18–50 in the greater New Haven, CT area who volunteered to participate in a study examining the role of stress and self-control at the Yale Stress Center as previously described [ 67 ]. Briefly, participants were recruited via advertisements online, in local newspapers, and at a community center between 2008 and 2012. Participants were excluded if they had a substance use disorder (not including nicotine) as assessed via the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (SCID-I for DSM-IVTR), were pregnant, had a chronic medical condition (e.g, hypertension, diabetes, hypothyroidism), or were unable to read English at or above the 6th grade level. Participants were also excluded if they had a concussion with loss of consciousness greater than 30 minutes, another head injury such as documented traumatic brain injury or another injury with documented lasting deficits, or were using any prescribed medications for any psychiatric or medical disorders. Breathalyzer and urine toxicology screens were conducted at each appointment to ensure the participants were drug-free. Of a total of 1000 potential participants who underwent initial screening for eligibility, epigenetic data combined with physiologic and behavioral data were available on 444, who comprised the current sample. All participants provided written and verbal informed consent to participate, and the research protocol was reviewed and approved by the Yale IRB.

Initial assessment and measurement of physiologic parameters

All eligible subjects met with a research assistant for two intake sessions to complete a physical health review with the Cornell Medical Index (CMI, [ 68 ]), structured clinical interview for diagnoses (SCID) of DSM-IVTR psychiatric illnesses, cumulative stress interview, self-report assessments and a separate morning biochemical evaluation after fasting overnight. The structured clinical interview was performed by masters’ or doctoral level clinical research staff. Fasting insulin and glucose were obtained and Cortisol was assessed at four time-points, spaced 15 min apart beginning at 7:30 AM after overnight fasting and collected while participants were in a quiet and comfortable laboratory setting at the Yale Stress Center. Participants were financially compensated for participating in the study.

Psychological measures

Cumulative stress was assessed using the Cumulative Adversity Inventory (CAI, [ 69 ]), a 140-item multifaceted interview-based assessment of life events and subjective stress through which trained interviewers asked participants about specific stressful events that occurred during their lifetime, which comprised the subscales of major life events, life trauma events and recent life events. For purposes of scoring, a “yes” to the specific stressful event occurring led to a “1” and a sum of all the “yes” endorsements comprised the subscale score for these events subscale. The final subscale of chronic stress was the participant’s own sense of feeling overwhelmed and unable to manage the events for the other subscales listed. This was rated on a “not true”, “somewhat true”, or “very true” scale, with assigned scores of 0, 1, and 2, respectively. The final score is a sum of these values for the chronic stress subscale. The CAI-total score was a sum of each of the subscale score with a higher score indicating a higher overall level of lifetime cumulative stress. The CAI has been demonstrated to have excellent overall reliability as reported in previous research [ 12 , 26 , 70 , 71 , 72 ]. In our population for this study, the alpha reliability is 0.86. It has been previously shown to predict cumulative stress related brain volume reductions and sensitized stress functional responses as well as prediction of physical, metabolic and behavioral responses [ 26 , 70 , 71 , 72 ].

Emotion regulation was assessed using the Difficulties with Emotion Regulation Scale (DERS, [ 65 ]), which is a 41-item trait-level measure that assesses across domains of lack of emotional awareness, goals, clarity, strategies, acceptance, and impulse control in managing emotions. Higher scores on the DERS correspond to lower ability to regulate emotion. Alpha reliability has been reported to be >0.90 for the total score, and ≥0.80 for the sub-scores [ 65 ]. In this population, the alpha reliability is 0.92.

Self-control was assessed using the Self-Control Survey-Brief (SCS-B, [ 66 ]), which is a 13-item scale that assesses overall self-control. A higher score on the SCS-B suggests a stronger level of self-control. There are no sub-scores provided by the SCS-B, and the overall SCS-B has been reported to have an alpha reliability >0.80 [ 66 ]. The alpha reliability in this study is 0.85.

The Cornell Medical Index (CMI) was used to assess for participants’ current health. It is a 195-question interview that captures both physical and psychological health symptoms, and has been validated as an indicator for current general health in many studies [ 68 , 73 , 74 ]. A higher score on the CMI suggests more symptoms and worse overall health. The alpha reliability of the total CMI is 0.94. The psychological subscore has an alpha reliability of 0.92, and the biological subscore has a reliability of 0.90.

Cronbach alpha reliabilities for each of the scales described above were obtained using the alpha function in the R psych package [ 75 ].

DNA methylation and epigenetic clock analysis

DNA for epigenetic analysis was collected from whole blood samples as previously described [ 67 ]. Briefly, all samples were profiled using Illumina Infinium HumanMethylation450 Beadchips, which covers 96% of CpG islands and 99% of RefSeq genes. Quality control on these data are as previously published [ 67 ]. They are described in brief below:

Probe QC : To ensure high-quality data, we set a more stringent threshold of P  < 10 –12 . Intensity values showing P  > 10 −12 were set as zero. Additionally, we removed 11,648 probes on sex chromosomes and 36,535 probes within 10 base pairs of single-nucleotide polymorphisms. Finally, a total of 47,791 probes were removed and the remaining 437,722 probes were used for further analysis.

Sample QC : Using a detection P value < 10 –12 , one sample showing a call rate < 98% was excluded from analysis. Five samples showing sex discrepancy between the methylation predicted sex and self-reported sex were also excluded from analysis.

Data processing and normalization : Data processing and normalization were performed using the recently published protocol (Lehne et al., 2015). We first perform background correction and within-array normalization to the original green/red channel intensity data using the preprocessIllumina function in the minfi R package. The processed data were transformed to M/U methylation categories. Next, we separately performed between-array-normalization with the quantile method using the normalizeBetweenArrays function in the limma R package (version 3.26.2) after dividing the data matrix into 6 independent parts: Type I M Green, Type I M Red, Type I U Red, Type I U Green, Type II Red, Type II Green. The normalized data were merged and the beta value at each CpG site was determined.

After obtaining beta values, epigenetic clock analysis was performed as described in Lu et al. using the New Methylation Age Calculator at https://dnamage.genetics.ucla.edu/new [ 51 ]. Data were normalized as per their protocol, and the advanced analysis option was used. We focus on GrimAge acceleration (GAA), which is defined as the residuals of a linear correlation of GrimAge to chronologic age. No effects of array batch on GAA were observed (Supplementary Fig. 1 ).

The analyses herein were performed without accounting for individual variations in cell types. The Houseman method was used to determine cell type proportion [ 76 ], and the inclusion of cell fractions as covariates in a linear model does not impact the primary conclusions of this paper (see Supplementary material).

Statistical analysis

Data organization and analysis were conducted using R 3.6.3 [ 77 ] and RStudio. Linear regressions were first implemented to examine univariate associations between independent and dependent variables. Multivariable linear regressions adjust for demographic (sex, race, years of education, marital status, income) and behavioral (smoking, alcohol use, and BMI) covariates unless otherwise stated. These covariates were selected due to prior work demonstrating a relationship to epigenetic aging. Chronologic age is incorporated into the model as part of the calculation of GAA (the residual of GrimAge regressed upon chronologic age). There was no significant correlation between chronologic age and GAA. Analyses of the relationship between CAI, GAA, psychological and physiologic variables were performed in both the univariate unadjusted model and the multivariate adjusted model accounting for demographic and behavioral measures, but except when the conclusions differ, statistical values in the text represent the multivariate models for simplicity. CAI, DERS, and SCS were mean-centered to address issues of collinearity (particularly regarding individual regression coefficients) when assessing for moderation.

All tests were two-tailed with alpha set at 0.05. Statistical significance in both standard linear regressions and moderation analyses were assessed from t values. R 2 reported on plots represent the simple relationship between the stated variables, while adjusted R 2 values in the text represent the model. Partial η 2 values represent the effect size for the specific variable, with a value >= 0.01 typically indicating a small effect, >= 0.06 a medium effect, and >= 0.14 a large effect [ 78 ]. Wilcoxon signed-rank test was used to compare data between sexes. Mediation analysis was performed to determine if stress impacts GAA via behavioral and physiologic factors. Simple mediation effects were calculated via R using 10,000 simulations without bootstrapping using the mediation package [ 79 ]. Mediation was considered significant if the proportion mediated was greater than 0 with an alpha of 0.05. Serial mediation was calculated via R using the Lavaan package [ 71 ], with an indirect effect defined as the product of the coefficients of the effect of stress on BMI, of BMI on HOMA, and of HOMA on GAA. Assessment of the individual variables’ attributable GrimAge acceleration as well as confidence intervals were calculated using the Emmeans package using unadjusted pairwise comparisons.

Demographics and clinical characteristics

As shown in Table 1 , study participants were healthy and without evidence of medical or psychiatric diseases. The majority were non-smokers (79.6%), social drinkers with low risky alcohol intake screening scores (72.7% of participants have Alcohol Use Disorders Identification Test (AUDIT) < 8, and 91.7% < 15), and were not obese (74.5% of participants have a BMI < 30, 89.2% < 35). Both physical and psychological symptoms assessed on the Cornell Medical Index (CMI, [ 68 ]) were low, with 86% of participants scoring below the typical screening threshold of 30.

Cumulative stress predicts accelerated biological aging as measured by GrimAge

As expected, there was a high association between individuals’ chronologic age and GrimAge (Age: t  = 51.4, P  < 2e−16, adjusted R 2  = 0.856, Fig. 2A ). This relationship is not altered by inclusion of the covariates of smoking, alcohol use, BMI, race, sex, income, and years of education (Age: t  = 49.1, P  < 2e−16, partial η 2  = 0.848; model (GrimAge ~ Age + covariates) adjusted R 2  = 0.912), and this relationship remained significant accounting for cellular fractions (Supplementary Table 1 ). Also, using a univariate linear regression, greater cumulative stress as measured by the total Cumulative Adversity Index (CAI) score significantly predicted higher GAA (CAI: t  = 4.82  P  = 2.00e−6, η 2  = 0.050, adjusted R 2  = 0.0478, Fig. 2B ). While there were significant differences in GAA based on sex ( P  = 1.33e−7), both males (CAI: P  = 3.35e−4, adjusted R 2  = 0.0586) and females (CAI: P  = 3.12e−5, adjusted R 2  = 0.0652) demonstrated similar correlations between stress and GAA. Further analysis showed these results are consistent across CAI subscales, as well as with the Childhood Trauma Questionnaire and several of its subscales (Supplementary Table 2 ).

A Chronologic age significantly predicts GrimAge ( P  < 2e−16). B Cumulative stress total as measured by the CAI (CAI-Total) significantly predicts GAA before ( P  = 2.00e−6) and after accounting for covariates. C Higher insulin resistance (as measured by HOMA) shows a significant positive correlation with GAA before ( P  = 1.11e−8) and after accounting for covariates. D The Cortisol/ACTH ratio is negatively correlated with GAA before accounting for covariates ( P  = 2.39e−6), but not afterward. P and R 2 values in the figure represent simple univariate models (Y ~ X). In the main text, models are adjusted for covariates as stated.

After accounting for the covariates of smoking, alcohol use, BMI, race, sex, income, and years of education, the relationship between GAA and CAI remains significant (CAI: t  = 2.073, P  = 0.0388, partial η 2  = 0.010; model (GAA ~ CAI-total + covariates): adjusted R 2  = 0.3869); individual covariate effects shown in Supplementary Table 3 ). When considered as potential mediators of the relationship between stress and GAA, BMI (proportion mediated = 0.288, P  = 0.0042) and smoking (proportion mediated = 0.443, P  = 0.0030), but not alcohol use (proportion mediated = 0.001, P  = 0.931), show partial mediating effects (Supplementary Table 4 ).

Consistent with the underlying assumption that GAA is related to measures of health, GAA also predicted psychological and physical health symptoms as measured by the CMI (Supplementary Fig. 2A ; total CMI: t  = 3.449, P  = 6.18e−4, adjusted R 2  = 0.024).

Stress-related physiology is associated with GrimAge acceleration

Given the known relationship between cumulative stress and physiology, we assessed the relationship between the stress-related physiologic factors of insulin resistance and HPA-axis signaling and GAA. We found that higher HOMA (a measure of insulin resistance) significantly predicted GAA (Fig. 2C , HOMA: t  = 2.362, P  = 0.0186, partial η 2  = 0.013; model (GAA ~ HOMA + Covariates): adjusted R 2  = 0.389).

We then assessed whether cortisol/ACTH ratio changes impacted GAA. Indeed, low cortisol/ACTH ratio, a measure of adrenal sensitivity, was associated with GAA in a simple univariate model, (Fig. 2D , Cort/ACTH ratio: t  = −4.78, P  = 2.39e−6, η 2  = 0.049, adjusted R 2  = 0.0470), though this becomes non-significant when accounting for covariates (Cort/ACTH ratio: t  = −0.721, P  = 0.471, partial η 2  = 0.001; model (GAA ~ Cort/ACTH + Covariates): adjusted R 2  = 0.3816). We also find a significant association between stress and Cortisol/ACTH ratio (Supplementary Fig. 2B , CAI: t  = −2.146  P  = 0.0324; model (Cort/ACTH ratio ~ CAI + covariates): adjusted R 2  = 0.2197).

Emotion regulation moderates the relationship between stress and GrimAge acceleration directly

We then asked whether the relationship between cumulative stress and epigenetic aging was modulated by characteristics that contribute to an individual’s psychological resilience. We hypothesized that strong emotion regulation abilities would be protective against stress-related accelerated aging. We found that emotion regulation as assessed by the Difficulties in Emotion Regulation Scale (DERS, [ 65 ]) significantly moderated the relationship between GAA and CAI (Fig. 3A , CAI:DERS: F  = 11.22, P  = 8.82e−4, partial η 2  = 0.025; model (GAA ~ CAI X DERS + covariates): adjusted R 2  = 0.4004), such that poor emotion regulation significantly increased the effects of CAI on GAA. There was not a significant difference between males and females in emotion regulation ( P  = 0.0949).

A Individuals with stronger emotion regulation (as measured by lower DERS scores) suffer less GAA at high stress than individuals with poor emotion regulation before (GAA ~ CAI X DERS P  = 9.51e−5; GAA ~ CAI X DERS + Covariates: P  = 8.82e−4) and after accounting for covariates. For panel A, “good” represents the slope at the 25th percentile of DERS, “fair” at the 50th percentile, and “poor” the 75th percentile. B Better self-control (as measured by higher B-SCS scores) is protective against the effects of stress on GAA before accounting for covariates (GAA ~ CAI X SCS P  = 0.00226; GAA ~ CAI X SCS + Covariates: P  = 0.130), but not after including them in the model. C Stronger self-control moderates the relationship between stress and insulin resistance before (HOMA ~ CAI X SCS P  = 0.0115; HOMA ~ CAI X SCS + Covariates P  = 0.00732) and after accounting for covariates. For panels (B) and (C), “good” represents the slope at the 75th percentile of B-SCS, “fair” at the 50th percentile, and “poor” the 25th percentile.

Self-control moderates the association between stress and insulin resistance, which is associated with GrimAge acceleration

We next assessed whether psychological resilience in the form of self-control (as measured via the SCS-B, [ 66 ]) alters the association between cumulative stress and GAA. We found higher self-control is protective against the effects of stress on GAA before accounting for covariates, but the interaction became non-significant when covariates were accounted for (Fig. 3B , CAI:SCS: F  = 2.303, P  = 0.130, partial η 2  = 0.005; model (GAA ~ CAI X SCS + Covariates: adjusted R 2  = 0.3874).

Given the potential interplay between self-control, insulin resistance, and stress, we next asked whether self-control moderated the relationship between stress and HOMA. We observed that, even when covariates are accounted for, self-control moderates the positive relationship between stress and HOMA, with stronger self-control blunting their relationship (Fig. 3C , CAI:SCS: F = 7.263, P  = 0.00732, partial η 2  = 0.017; model (HOMA ~ CAI X SCS + Covariates: adjusted R 2  = 0.2871). Notably, self-control does not moderate the relationship between CAI and BMI (CAI:SCS: F  = 0.679, P  = 0.41). Self-control did not significantly differ between males and females ( P  = 0.0550).

Exploratory mediation analyses suggest stress influences GrimAge via BMI and HOMA

While our ability to draw causative inferences are limited by the cross-sectional nature of our data, we used mediation analyses to explore potential relationships between weight, insulin resistance, and GAA. We hypothesized that the effects of BMI on GAA may be mediated through insulin resistance. Indeed, mediation analysis suggested that a significant portion of the effect of BMI on GAA may be mediated through HOMA (Supplementary Fig. 3A , proportion mediated = 0.247, P  = 0.02). Given these findings, we next asked whether BMI and insulin resistance act sequentially to mediate the effects of stress on GAA. We identified a significant indirect effect, suggesting that stress may affect GAA through increased BMI and elevated insulin resistance (Supplementary Fig. 3B , indirect effect = 0.003; P  = 0.030), though there continues to be a significant direct effect of stress on GAA as well (direct effect = 0.034, P  = 0.009).

Cumulative stress and estimated change in GrimAge

Finally, we sought to identify the comparative contributions of our significant variables to GAA. To do this, we constructed a linear regression model using all demographic covariates (sex, race, marital status, education, income), behavioral covariates (smoking, alcohol, BMI), physiologic factors (HOMA, Cortisol/ACTH ratio), and psychological factors. In this model, we continue to see a significant interaction between stress and emotion regulation in relation to GAA (CAI:DERS t  = 3.424, P  = 0.000677, partial η 2  = 0.027; model (GAA ~ CAI-total X DERS + HOMA + Cort/ACTH ratio + SCS + Covariates): adjusted R 2  = 0.4056). Notably in this model, HOMA ( t  = 2.308, P  = 0.0215, partial η 2  = 0.012), BMI ( t  = 2.641, P  = 0.00857, partial η 2  = 0.016), and smoking ( t  = 10.47, P  < 2e−16, partial η 2  = 0.204) also demonstrate significant effects on GAA. The impact of the cortisol/ACTH ratio on GAA is not significant ( t  = −0.668, P  = 0.504, partial η 2  = 0.001), and its removal from the model does not impact any of the above conclusions.

Using this final linear model, we estimated the changes in GrimAge for each significant variable (Table 2 ) using estimated marginal means [ 80 ]. When comparing the effects of high stress (CAI-total: 75th percentile) versus low stress (CAI-total: 25th percentile) in those with poor emotion regulation (DERS: 75th percentile), stress was associated with half a year of aging independent of all other covariates and physiologic factors. However, when emotion regulation was strong (DERS: 25th percentile), stress did not independently predict GAA. Again comparing 75th versus 25th percentiles, BMI independently was related to an increase of 0.46 years of GrimAge, and HOMA for ¼ of a year. We also identified daily smoking (3.8 years), male sex (1.2 years), self-identifying as Black (1 year), and never having married (0.71 years) as covariates that significantly predicted accelerated GrimAge. When accounting for cellular fractions we see similar results regarding the relationships between stress, emotion regulation, and GAA. However, when accounting for cellular fractions, the associations between GAA and both HOMA and marital status become non-significant (Supplementary Table 5 ). Prior literature [ 51 ] suggests that GrimAge predicts the hazard ratio exponentially (HR = 1.1 GAA ). Thus, each additional year of GAA would be expected to increase the relative risk of death by approximately 10%.

In this study, we report novel findings that cumulative stress is associated with accelerated epigenetic aging in a healthy, young-to-middle-aged community sample, even after adjusting for sex, race, BMI, smoking, alcohol use, income, marital status, and education. Epigenetic aging was measured by GrimAge, a marker which has previously been associated with increased morbidity and mortality and correlates with physical and psychological health symptoms in our study. The relationship between stress and age acceleration is most prominent in those with poor emotion regulation and was related to behavioral factors such as smoking and BMI. Both stress and GAA were associated with changes in insulin resistance, which was moderated via self-control. These results suggest a relationship between stress, physiology, and accelerated aging that is moderated by emotion regulation and self-control. Overall, these findings point to multiple potentially modifiable biobehavioral targets of intervention that may reduce or prevent the deleterious effects of stress on aging and long-term health outcomes.

This study included a generally healthy, young-to-middle-aged community population, yet we still identified a significant relationship between cumulative stress and age acceleration. The population was taking no prescription medications for any medical conditions, nor were they suffering from current mental illnesses, including major depressive disorder or generalized anxiety disorder. The study includes individuals with obesity, as well as a small number of individuals with risky drinking levels as determined by the AUDIT scores. The frequency of these individuals in the sample is generally in line with those in a community population, and thus we included alcohol use and BMI as covariates to account for the impact of these variables on the results. Prior work has demonstrated that GrimAge better predicts mortality than other epigenetic clocks, and GrimAge predicts lifespan more accurately than self-reporting smoking history, demonstrating that GrimAge is a biologically meaningful and potentially clinically useful biomarker for health [ 51 , 64 ]. Our findings are consistent with recent work showing that those with significant trauma histories [ 59 , 81 ] or with diagnoses of mental illnesses, such as Bipolar disorder or MDD, may experience accelerated aging as measured by epigenetic clocks [ 57 , 81 , 82 , 83 , 84 ]. In particular, this study builds on previous findings by Zannas et al that demonstrated a relationship between trauma and epigenetic aging using the Horvath clock. However, to the best of our knowledge this is the first study to investigate the impact of cumulative stress on epigenetic aging in a healthy community sample without significant physical or mental illness. Also it is the first to our knowledge to identify factors that contribute to psychological resilience as potential modulators of such an effect. This opens the possibility that the distinction between the effects of stress on pathologic and non-pathologic samples may be along a continuum. It would be interesting to examine resilience characteristics in the population studied by Zannas et al to determine if there is a limit to the protective effects of psychological resilience. Thus, preventive interventions that decrease stress and improve resilience may be useful for maintaining long-term mental and physical health.

The relationship between stress and epigenetic aging appears to be modulated via specific psychological traits, including emotion regulation and self-control. Those with better emotion regulation and higher levels of self-control were observed to have less age acceleration even at similar levels of stress. Indeed, based on their GAA, our estimates indicate that the relationship between stress and GrimAge is as powerful as BMI, but only for those with poor emotion regulation. As these are skills that may be developed through specific psychological interventions [ 85 ], these results raise the possibility that building emotion regulation skills could result in improvements in epigenetic aging, morbidity, and mortality [ 86 ] for these populations. As this is a cross-sectional study, we are not able to address whether these relationships are causal. These novel cross-sectional findings provide support for potential future research that may assess whether such an intervention could positively impact epigenetic aging and other indices of long-term health outcomes. Other studies could also examine different aspects of resilience, such as cultural or environmental factors that contribute to resilience to determine if they also are protective against the effects of stress on epigenetic age acceleration. Future studies could also explore other physiologic mechanisms through which psychological resilience may influence epigenetic aging. Based on prior work, inflammation could be particularly important for this relationship. In particular, prior studies have found C-reactive protein [ 87 ] and IL-6 [ 88 ] to be related to emotion regulation and measures of health. The work by Gianaros et al suggests that neurologic activity of the dorsal anterior cingulate cortex may be involved as well.

The relationship between cumulative stress, epigenetic aging, and insulin resistance is of particular note given the prominence of insulin signaling in aging-related pathways [ 89 , 90 ], as well as current trials investigating metformin as a potential anti-aging drug [ 33 ]. In association with this body of work, our study suggests insulin resistance as at least one factor through which stress is associated with accelerated aging, even in a healthy population not suffering from diabetes. As this study is limited by its cross-sectional nature, any causal hypotheses regarding interactions between stress, BMI, insulin resistance, and aging will require longitudinal data to draw specific inferences beyond correlative relationships. Longitudinal studies would also enable prospective assessments of stress, which may be less subject to recall bias based on their current context. This study also identifies the cortisol/ACTH ratio as a potential point of connection between stress and epigenetic aging. However, this measure is somewhat limited in that it reflects an acute measure of the HPA axis, and this relationship becomes non-significant with the inclusion of our covariates. Future studies could utilize other, longer-term measures of HPA axis function such as hair cortisol to better characterize the relationship between stress, epigenetic aging, and the HPA axis.

Nonetheless, this study is the first to identify a clear relationship between cumulative stress and GrimAge acceleration in a healthy population, which suggests stress may play a role in accelerated aging even prior to the onset of chronic diseases. Notably, this relationship was strongly moderated by resilience factors, including self-control and emotion regulation. We also identified smoking, BMI, insulin signaling, and potentially HPA signaling as mediators of this response. However, even when accounting for all these factors as well as demographic covariates such as race, cumulative stress continues to demonstrate a significant impact on GAA, suggesting other mechanisms relating stress to aging not identified herein are also present.

Code availability

R scripts utilized for data analysis are available by contacting the authors directly.

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Acknowledgements

The authors would like to acknowledge the Yale Center of Genome Analysis for DNA methylation profiling. Funding for this study is from NIH Common Fund UL1-DE019586 (R.S.), PL1-DA24859 (R.S.), R01-AA013892 (R.S.), NIH R01DA047063 (K.X.), NIH T32MH019961 (Z.M.H.), NIH R25MH071584 (Z.M.H.). These data were presented at the SOBP virtual conference in April 2021 as a poster.

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Z.M.H., K.X., and R.S. conceptualized the project. Z.M.H. and N.F. performed the data analysis, with recommendations from K.X. and R.S. Z.M.H. produced the figures and tables. Z.M.H. wrote the manuscript, and all authors contributed to and edited the manuscript.

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Harvanek, Z.M., Fogelman, N., Xu, K. et al. Psychological and biological resilience modulates the effects of stress on epigenetic aging. Transl Psychiatry 11 , 601 (2021). https://doi.org/10.1038/s41398-021-01735-7

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ORIGINAL RESEARCH article

Resilience building in students: the role of academic self-efficacy.

• Psychology and Public Health, University of Salford, Salford, UK

Self-efficacy relates to an individual's perception of their capabilities. It has a clear self-evaluative dimension leading to high or low perceived self-efficacy. Individual differences in perceived self-efficacy have been shown to be better predictors of performance than previous achievement or ability and seem particularly important when individuals face adversity. The study investigated the nature of the association between academic self-efficacy (ASE) and academic resilience. Undergraduate student participants ( N = 435) were exposed to an adverse situation case vignette describing either personal or vicarious academic adversity. ASE was measured pre-exposure and academic resilience was measured post-exposure. ASE was correlated with, and a significant predictor of, academic resilience and students exhibited greater academic resilience when responding to vicarious adversity compared to personal adversity. Identifying constructs that are related to resilience and establishing the precise nature of how such constructs influence academic resilience will assist the development of interventions aimed at promoting resilience in students.

Introduction

Psychological resilience.

A shift in emphasis in mental health policy to include promotion of positive mental health as a preventative measure ( WHO, 2005 ), together with the identification of resilience and coping as one of eight positive mental health grouping ( Parkinson, 2008 ), underlines the value of studies examining resilience. Abiola and Udofia (2011) suggest resilience is associated with increased quality of life, wellbeing and functional capacity in times of adversity. Although there is an intuitive appreciation for the “meaning” of resilience and what it infers (about the individual), consensus in defining psychological resilience, both conceptually and operationally as a measurable construct, has yet to be reached. As Friedland (2005) notes, perspectives on resilience are highly diverse and the concept of resilience is highly elusive. In an attempt to illustrate the concept, Gilligan (2001) uses the example of a resilient child as a child who does better than they ought to, bearing in mind what has happened to them. Friedland (2005) goes on to discuss resilience as inferring hardiness, toughness, and resistance, along with—somewhat paradoxically—elasticity and flexibility. This suggests that resilience is both multi-faceted and multi-leveled and the range of available definitions reflects this in both their depth and their breadth. Resilience is described by Hamill (2003) as competence in the face of adversity and by Gilligan ( 2001 , p. 5) as “a set of qualities that help a person to withstand many of the negative effects of adversity.” Pooley and Cohen ( 2010 , p. 34) define resilience as “the potential to exhibit resourcefulness by using internal and external resources in response to different contextual and developmental challenges….” Garmezy and Masten ( 1991 , p. 459) refer to resilience as “the process of, capacity for, or outcome of successful adaptation despite challenging circumstances.” Abiola and Udofia (2011) offer a fuller account, discussing resilience in terms of inner strength, competence, optimism, flexibility, and the ability to cope effectively when faced with adversity, minimizing the impact of risk factors, such as stressful life events, and enhancing the protective factors, such as optimism, social support, and active coping, that increase people's ability to deal with life's challenges.

Although seemingly diverse, most definitions of resilience feature adaptive, resourceful and innovative enabling responses to adversity, threat or challenge as a core element. As such, resilience is considered an asset or strength, a desirable and advantageous quality, characteristic or process that is likely to impact positively on aspects of an individual's performance, achievement, health, and wellbeing ( Bartley et al., 2010) .

As is common with many psychological constructs—self-efficacy for example ( Bandura, 1997 )—, there is debate around the existence and relevance of a global resilience construct. Instead, there is a strong argument for resilience to be considered—and measured—as a context-specific construct. Riley and Masten (2005) explain the need to contextualize resilience on the basis that judgments about risk and adversity relate directly to events and contexts, as do evaluations of competencies and outcomes. Both Liddle (1994) and Waxman et al. (2003) refer to the need to contextualize resilience in order to generalize findings from resilience studies and in order to consider specific practical implications for building resilience. The present study examines resilience in the context of education and learning (i.e., academic resilience), considering resilience as an asset and seeking to identify factors that may contribute to resilience promoting interventions for students, suggested by Zautra (2009) to have long-term benefits.

Academic Resilience

Wang et al. (1994) refer to academic resilience as an increased likelihood of (academic) success despite environmental adversities. Resilient students are described by Alva (1991) as those who maintain high motivational achievement and performance even when faced with stressful events and conditions that place them at risk of poor performance and by Waxman et al. (2003) as those who succeed at school despite the presence of adverse conditions.

As is the case with general resilience, work focussing on academic resilience has led to the emergence of apparently distinct yet related concepts and constructs, each aiming to address a seemingly similar issue. Although drawing some explicit distinctions between their own constructs and resilience ( Perkins-Gough, 2013 ), both Duckworth and Dweck provide significant contributions to the field of academic resilience with their work on “grit” and “mindset.” Duckworth describes grit as an individual's tendency to sustain interest, passion, effort and persistence toward achieving long-term future goals (despite challenges and failures) and reports grit as a better predictor of academic success than IQ ( Duckworth et al., 2007 ; Duckworth, 2013 ) or talent ( Duckworth and Quinn, 2009 ). Dweck's (2006 , 2010 ) work on mindset has led to the identification of two types of mindset, fixed and growth. A fixed mindset describes individuals with fixed beliefs regarding their level of intelligence and ability, which they believe remain stable. A growth mindset instead describes individuals who view their intelligence and ability simply as a basis for development and believe that challenges, including failure, are opportunities to develop their capacity for success through effort and practice. The influence of mindset is emphasized further by Snipes et al. (2012) , who consider a growth mindset to be a major contributory factor in the development of grit. Despite noted dissimilarities—Duckworth considers resilience to be only one factor explaining grit ( Perkins-Gough, 2013 )—there are clear overlaps between academic resilience and the constructs proposed by Duckworth and Dweck, and their relevance is illustrated by Farrington et al. (2012) who reports that the combination of a growth mindset and grit in students is been associated with higher academic grades.

Another construct, closely related to academic resilience, proposed by Martin and Marsh (2008 , 2009) is academic buoyancy. Described as the “capacity to overcome setbacks, challenges, and difficulties that are part of everyday academic life.” ( Martin, 2013 , p. 488) it is seen as distinct from academic resilience, which instead relates to the capacity to overcome significant adversity that threatens a student's educational development. Martin (2013) does present evidence that whilst buoyancy and resilience are related, buoyancy better predicts low-level negative outcomes and resilience better predicts major negative outcomes, which aligns with Martin and Marsh's (2008) earlier description of buoyancy as reflecting “everyday” academic resilience.

Waxman et al. (2003) suggest that studying resilient students will provide important implications for improving the education of students at risk of academic failure and evidence already exists supporting the relevance of academic resilience. McLafferty et al. (2012) reported that both resilience and emotional intelligence predicted coping at university, with resilience as the only significant unique predictor of coping subscales for grades, attendance, and studying. Furthermore, Abiola and Udofia (2011) reported higher perceived stress, anxiety and depression in low resilience medical students following completion of a major professional examination.

Waxman et al. (2003) note that resiliency refers to factors and processes that limit negative behaviors associated with stress and result in adaptive outcomes in the presence of adversity. They discuss the value of resilience studies that identify differences between resilient and non-resilient students and that focus on alterable factors to design more effective educational interventions. They suggest that focusing on educational resilience and those factors that can be altered to promote resilience may help address the gap in achievement between those students who are successful and those who are at risk of failure. Like Wagnild (2009) , Waxman et al. (2003) further suggest that rather than being fixed, academic resilience can be promoted by focussing on alterable factors including social competence, problem-solving skills, autonomy, a sense of purpose ( Bernard, 1993 ), motivation and goal orientation, positive use of time, family life, and learning environment ( McMillan and Reed, 1994 ). The potential for building resilience, together with Munro and Pooley's (2009) suggestion that resilience may mediate adversity and success in university students and Hamill's (2003) prioritizing of self-efficacy over other resilience factors, provides the major premise for the present study examining academic self-efficacy (ASE) as a factor influencing student responses to academic adversity.

Resilience and Self-efficacy

Waxman et al. (2003) proposes that academic resilience research needs to examine indicators of resiliency in order to identify what processes can promote protective mechanisms and calls for more affective and motivational training for students to assess their impact on students' affective and motivational outcomes. Aiming to provide a more “expansive” analysis of the factors related to academic resilience, Martin and Marsh (2006) reported self-efficacy, planning, persistence, anxiety, and uncertain control as predictors of academic resilience. Using class participation (behavioral) and enjoyment at school (cognitive-affective) as educational outcome constructs and general self-esteem (global-affective) as a psychological outcome construct, Martin and Marsh hypothesized that the outcome constructs were consequential to students' capacity to effectively deal with challenge, adversity and setbacks experienced in a school setting. As hypothesized, academic resilience was the strongest—relative to the other five motivational and engagement factors—predictor of each of the outcome measures. Analysis to determine students' profiles according to academic resilience revealed that resilient students were high in self-efficacy, persistence and planning and low in anxiety and uncertain control. Hamill (2003) also reported self-efficacy as an important characteristic that distinguished resilient and non-resilient 16–19 year old students.

The pursuit of those factors that distinguish resilient from non-resilient individuals and the promotion of resilience have been at the center of existing research in the field resilience ( Hamill, 2003 ). There is sufficient evidence indicating that self-efficacy is one resilience factor worthy of further study in this respect. Self-efficacy emerged as a central facet in Albert Bandura's Social Cognitive Theory, where is it described as “the belief in one's capabilities to organize and execute the course of action required to manage prospective situations” ( Bandura, 1995 , p. 2). In educational studies, individual differences in perceived self-efficacy have often been shown to be better predictors of performance than either previous achievement or ability ( Cassidy, 2012 ).

Like resilience, self-efficacy is context specific and seems particularly important when individuals face adversity, when positive self-efficacy beliefs are associated with increased motivation and perseverance ( Bandura, 1997 ; Bandura et al., 2001 ) and an increased likelihood of rejecting negative thoughts regarding own capabilities ( Ozer and Bandura, 1990 ).

Self-efficacy is considered to be the foundation of human agency ( Bandura et al., 1999 ) and is referred to as an important protective factor regulating human functioning and emotional wellbeing through cognitive, motivational, affective, and selective processes ( Hamill, 2003 ). And whilst Bandura (1993) does describe how self-efficacy operates to contribute toward academic development—stating that students' beliefs in their efficacy to regulate their own learning and master academic activities determine their aspirations, level of motivation and academic accomplishment—there is a lack evidence-based detail accounting for exactly what high self-efficacious individuals do that impacts positively on academic outcomes; as noted by Hamill (2003) , despite an abundance of self-efficacy focussed research, relatively little work has examined how self-efficacy relates to resilient behaviors exhibited in response to adversity.

Present Study

Operationalizing academic resilience as students' cognitive-affective and behavioral responses to academic adversity, the present study seeks to establish examples of context-specific resilience factors and resilience responses to academic adversity. Self-efficacy has been identified as a key construct in previous studies examining factors affecting academic achievement (e.g., Cassidy, 2012 ), where high self-efficacy is commonly reported as associated with better academic performance. What has not been clearly established in these studies are the specific responses of self-efficacious students to instances of academic adversity, when self-efficacy beliefs are particularly relevant because of their association with increased motivation and perseverance ( Bandura, 1997 ) and resistance to negative thought ( Ozer and Bandura, 1990 ). Hamill (2003) has explored this issue but using generalized measures of self-efficacy and coping responses in the context of general stressful life events in a small sample of 16–19 year old students—limitations which Hamil partly acknowledges. Hamil reported an association between self-efficacy and resilience, adding support to the merits of the present study and its aim of uncovering differences in context-specific resilience responses adopted by self-efficacious and non-self-efficacious students, and the study's longer-term objective of promoting resilient responses in students.

Riley and Masten (2005 , p. 13) define resilience as “referring to patterns of positive adaptation in the face of adversity…,” and describe resilience as requiring “that significant adversity or threat to adaptation or development has occurred” and “that functioning is okay, either because adequate adaptation was sustained over a period of adversity or because recovery to adequate functioning has been observed.” In order to represent the key constituents of resilience (i.e., adversity and positive adaption) in a context-specific and authentic manner to serve the purposes of the study, an academic adversity case vignette and a response to academic adversity scale (Academic Resilience Scale-30) were developed [see Section Academic Resilience Scale-30 (ARS-30)].

The content of the case vignette was intended to represent adversity in a context-specific academic setting that undergraduate students would consider authentic despite its hypothetical nature. The vignette describes academic failure and its wider impact as an example of authentic adversity for students. Although there is some debate in the existing literature on the specific effects of, and perceptions of, negative feedback (e.g., Kluger and DeNisi, 1996 ), reference in the vignette to failure and the wider negative impact of such failure was considered to be sufficient to instill academic adversity. There are two versions of the vignette presented in Section Academic Resilience Scale-30 (ARS-30), personalized and vicarious . The personalized vignette asks that participants imagine that they are personally facing adversity and how they would respond, whilst the vicarious vignette asks participants to imagine that a fellow student is facing adversity and how that student should respond. The vicarious vignette was developed in order to explore any differences between responses to personal adversity and responses advocated for a fellow student facing adversity, and to examine in what way self-efficacy beliefs are associated with any differences. Gaining such insight may be valuable for resilience building interventions, whereby any differences in personal and advocated responses can be used to highlight self-limiting responses or belief systems that may also limit students' capacity for acting in advocate roles, including peer-assisted learning programmes.

Based on previous studies it is anticipated that findings will reveal a positive relationship between ASE and academic resilience, although it is unclear which of the 30 responses to academic adversity will present as most pivotal in defining differences in academic resilience between lower and higher ASE students. Because self-efficacy is a “self” construct most closely related to personal functioning, it is anticipated that any association between self-efficacy and resilience will be more pronounced in responses to the personal adversity vignette as compared to the vicarious adversity vignette.

Participants and Design

The sample comprised 435 British undergraduate students (see Tables 1 , 2 ). The study adopted a self-report questionnaire-based design with correlational and between-subjects components. Academic self-efficacy and academic resilience were measured during a single data collection point in participants' first, second, or third year as undergraduates. Gender, age, and year of study data were also collected. Whilst the gender bias evident within the sample was not desirable, that over 80% of the sample were female is representative of a typical student intake, at least in psychology ( Bourne, 2014 ).

Table 1. Total sample details .

Table 2. Sample details by vignette group .

General Academic Self-efficacy Scale (GASE)

This is 23 item context-specific scale measuring student ASE. The General Academic Self-Efficacy Scale was adapted from the Health Student Self-Efficacy (HSSE) Scale originally developed by Eachus (1993) as a measure of self-efficacy beliefs in students on health-related courses involving clinical training and practice. Cassidy and Eachus (2002) revised the HSSE scale, removing reference to clinical placements, and developed the GASE scale for use with general undergraduate student populations. Participants record their level of agreement with each of the 23 items along a 9-point Likert scale from strongly agree to strongly disagree. The scale contains both positively and negatively worded items, examples of which include “I know I have the ability to complete this course successfully” and “I have some doubts about my ability to grasp some of the topics taught on this course.” Scores for negatively worded items are reversed so that a high GASE score indicates high (or positive) ASE. Scores for the 23 items are summed providing a total scale score between 23 and 207. Cassidy and Eachus (2002) report high internal (α = 0.86) and external ( r = 0.71) reliability for the GASE scale and construct validity is further demonstrated through significant correlations with academic locus of control and computer user self-efficacy. A similarly high alpha (α = 0.84, N = 434) is reported in the present study.

Academic Resilience Scale-30 (ARS-30)

In the absence of a suitable measure of academic resilience, the ARS-30 was developed as a context-specific measure of student response to academic adversity. Scale items represent a sample of relevant positively and negatively phrased behavioral and cognitive-affective responses that participants have to rate as likely or unlikely along a 5-point Likert scale following exposure to the personal or vicarious adversity case vignette:

Personal Vignette : You have received your mark for a recent assignment and it is a “fail.” The marks for two other recent assignments were also poorer than you would want as you are aiming to get as good a degree as you can because you have clear career goals in mind and don't want to disappoint your family. The feedback from the tutor for the assignment is quite critical, including reference to “lack of understanding” and “poor writing and expression,” but it also includes ways that the work could be improved. Similar comments were made by the tutors who marked your other two assignments.

Vicarious Vignette : John has received a mark for a recent assignment and it is a “fail.” The marks John received for two other recent assignments were also poorer than he would want as he is aiming to get as good a degree as he can because he has clear career goals in mind and doesn't want to disappoint his family. The feedback John received from the tutor for the failed assignment is quite critical, including reference to “lack of understanding” and “poor writing and expression,” but it also includes ways that the work could be improved. Similar comments were made by the tutors who marked John's other two assignments.

Scoring of positively phrased items was reversed so that a high ARS-30 score indicated greater academic resilience. Cronbach's alpha for the combined (α = 0.89, N = 432), personalized (α = 0.88, n = 224) and vicarious vignette (α = 0.85, n = 208) all reached acceptable levels indicating internal reliability and construct validity ( Nunnally and Bernstein, 1994 ). Analysis of the relationships between ARS-30 scores and ASE and differences between personal and vicarious responses to adversity further supported the construct validity of the ARS-30 as a measure of academic resilience (see Section Results).

Exploratory factor analysis [principle component with oblique (promax) rotation] was conducted to explore the structure of the ARS-30. Sampling adequacy was verified (KMO = 0.91) and whilst initial analysis revealed seven factors with eigenvalues of 1.0 or above ( Kaiser, 1960 ) explaining 55.75% of the variance, the scree plot inflection ( Cattell, 1966 ) supported retention of only three factors ( Hatcher, 1994 ; Stevens, 2002 ). The three factor model explained 40% of the variance with all items—except one, which loaded at 0.29—loading above 0.3 ( Field, 2014 ). Interpretation of Item-factor clustering suggests that factor 1 represents positive or adaptive responses to adversity, factor 2 represents negative or non-adaptive responses to adversity and factor 3 represents long-term future aspirations. Thus, factors 1 and 2 may simply represent two aspects of the same underlying generalized academic resilience construct. This is partly supported by Schmitt and Stults (1985) and Spector et al. (1997) who report that reverse-phrased items commonly load on different factors, even in the absence of multiple constructs, and by the inter-factor correlation (−0.45) between factors 1 and 2. That factor 3 aligns with closely associated and relevant constructs such as Duckworth's “grit,” which has its basis in long-term goals, suggests that a three factors solution presents an interpretable solution to the ASR-30.

The study was carried out in accordance with the recommendations of the British Psychological Society Code of Ethics and Conduct and the Research, Innovation and Academic Engagement Ethical Approval Panel, University of Salford with written informed consent from all subjects in accordance with the Declaration of Helsinki.

After completing the GASE scale, participants were randomly assigned to one of the adversity vignette conditions and completed the ARS-30 (personal or vicarious). Data collection was anonymous to improve the validity of responses. A median-split approach was used to create discrete groups according to scores on the GASE. Participants with scores equal to or below the GASE sample median of 148 were assigned to the lower ASE group, while participants scoring above the median were assigned to the higher ASE group. Whilst the median-split approach is criticized on the basis of loss of statistical power and the potential for spurious outcomes in cases of multiple variables ( MacCallum et al., 2002 ; Irwin and McClelland, 2003 ), the approach has received support in terms of producing meaningful findings that are understood by, and accessible to, a wider audience where statistical power and effect are not necessarily reduced ( Farrington and Loeber, 2000 ). Thus, the use of dichotomization here is defended on the basis that correlational and regression analysis were also performed for the main analysis using GASE scores as a continuous variable; that the mean difference between groups (30.3) provided, it is suggested, sufficient numerical distance between groups; and the wish to illustrate, in a meaningful way, distinctions between groups in terms of specific responses to adversity.

Significant positive correlations between ASE and academic resilience were observed for the combined vignette groups (medium effect size r = 0.34, Cohen, 1988 ) and for the personal (large effect size r = 0.51) and vicarious vignette groups (small effect size r = 0.21) separately. Academic self-efficacy was a significant predictor of academic resilience explaining 26.2% of variance in resilience in the personal vignette group, 4.6% in the vicarious vignette group, and 14% in the combined groups (see Table 3 ).

Table 3. Zero order correlations and regression analysis with academic self-efficacy (ASE) as a predictor of academic resilience .

A 2(vignette: personal vs. vicarious) × 2(ASE: lower vs. higher) between-subjects factorial ANOVA was conducted to examine differences in academic resilience between personal and vicarious vignette groups as a function of ASE (see Table 4 ).

Table 4. Mean academic resilience scores by vignette group and academic self-efficacy (ASE) Group .

There were significant main effects for vignette group [ F (1, 427) = 101.91, p < 0.001, d = 0.96], such that the vicarious vignette group reported significantly higher academic resilience ( M = 128.51, SD = 11.47) than the personal vignette group ( M = 116.25, SD = 14.07), and for ASE group [ F (1, 427) = 38.26, p < 0.001, d = 0.58], with the higher ASE group reporting significantly higher academic resilience ( M = 126.16, SD = 11.99) than the lower ASE group ( M = 118.20, SD = 15.20). A significant interaction effect [ F (2, 427) = 10.9, p < 0.001, d = 0.33] indicated that the influence of ASE on increasing academic resilience was significantly greater in the personal vignette group, where the effect size was large ( d = 0.86), than in the vicarious vignette group, where the effect size was small ( d = 0.30) (see Figure 1 ).

Figure 1. Mean academic resilience by ASE group vignette type .

Both lower and higher ASE groups scored higher academic resilience when responding to the vicarious vignette than when responding to the personal vignette, though the effect size was larger for the lower ASE group (large ES d = 1.21) than for the higher ASE group (medium ES d = 0.71).

Table 5 shows ASR-30 (personal vignette) mean item scores by ASE group (lower and higher). A One-way MANOVA was performed on these data with ASE group (lower vs. higher) as the independent variable and ASR-30 item scores as the dependent variables. There was a significant multivariate effect [ F (1, 222) = 2.971, p < 0.001] and significant univariate effects. Significant univariate effects are denoted by “*”and reflect scores indicating significantly higher academic resilience for the higher ASE group on all items except items 1, 6, 14, 26, and 29, where any differences were non-significant ( p > 0.05). Effect sizes were medium ( d ≥ 0.5) for 12 of the items and small ( d ≥ 0.2 < 0.05) for the remaining 13 items where a significant group difference was reported.

Table 5. Academic resilience scale (personal vignette) item summary statistics by academic self-efficacy (ASE) group .

Table 6 shows ASR-30 (vicarious vignette) mean item scores by ASE group (lower and higher). A One-way MANOVA was performed on these data with ASE group (lower vs. higher) as the independent variable and ASR-30 item scores as the dependent variables. The multivariate effect was non-significant [ F (1, 205) = 0.659, p >0.05]. Significant univariate effects were only found for items 6, 11, 15 and 24 ( p < 0.05) and reflect scores indicating significantly higher academic resilience for the higher ASE group, although effect sizes were small or minimal ( d < 0.2).

Table 6. Academic resilience scale (vicarious vignette) item summary statistics by academic self-efficacy (ASE) group .

Table 7 shows ASR-30 mean item scores by vignette group. A One-way MANOVA was performed on these data with vignette group (personal vs. vicarious) as the independent variable and ARS-30 item scores as the dependent variables. There was a significant multivariate effect [ F (1, 430) = 14.929, p < 0.001] and significant univariate effects. Significant univariate effects are denoted by “*” and “**” and reflect scores indicating significantly higher academic resilience for the vicarious group on all items except items 5 and 19 where academic resilience was significantly lower in the vicarious group (with minimal or small effect size) and items 1, 2, 10, 11, 13, 15, and 17, where any differences were non-significant ( p >0.05). Effect sizes were large ( d ≥ 0.8) for one item, medium ( d ≥ 0.5) for seven items, small ( d ≥ 0.2) for 12 items, and minimal ( d < 0.2) for the remaining three items where a significant group difference was reported.

Table 7. Academic resilience scaleitem mean scores by vignette group .

Figure 2 shows that while the difference in mean academic resilience scores between the personal and vicarious vignette groups was significant [ t (430) = 9.908, p < 0.001], with a large effect size ( d = 0.96), there was no significant difference in ASE scores [ t (432) = 0.356, p > 0.05].

Figure 2. Mean academic resilience by vignette group .

Age, Gender, and Year of Study Analysis

Gender and year of study analysis did not reveal any significant differences in academic resilience ( p > 0.05). Correlational analysis did not reveal any significant association between age and academic resilience ( p > 0.05), although a small significant correlation between age and ASE [ r (429) = 1.58, p < 0.001] was reported.

Overall results support the hypothesis that ASE is associated with, and a predictor of, academic resilience. Significant positive correlations between ASE and academic resilience were reported for both the personal and vicarious vignettes, although effect size was large for the personal vignette group and small for the vicarious vignette group. Analysis of ASE as a predictor of academic resilience also led to significant results for each of the vignette groups, with the greatest variance in academic resilience (26.2%) accounted for in the personal vignette group compared to only 4.6% in the vicarious group. Although previous studies have reported self-efficacy as an important contributory factor for resilience ( Hamill, 2003 ; Martin and Marsh, 2006 ), the present study offers additional insight into the context-specific interplay of these constructs. As advocated by Pajares (1996) and by Riley and Masten (2005) , Liddle (1994) and Waxman et al. (2003) , both self-efficacy and resilience were measured as context-specific constructs and in relation to—it is argued here—an authentic adverse situation and relevant adaptive responses. In both general and context-specific terms, findings support the relevance of self-efficacy beliefs to individual psychological resilience; having positive self-efficacy beliefs is likely to contribute toward increased resilience in students.

Once a relationship between ASE and academic resilience was established, further analysis sought to identify differences between lower and higher self-efficacy students in their specific responses to adversity. As anticipated, higher self-efficacy students reported significantly higher academic resilience for both case vignettes, although a significant interaction effect indicated greater influence of self-efficacy for the personal vignette, where the effect size was large, than for the vicarious vignette, where the effect size was small. The greater influence of self-efficacy on personal resilience is unsurprising in light of Bandura's (1993) account of self-efficacy as a mechanism of personal agency that makes causal contributions to own functioning. Analysis of responses to individual items on the Academic Resilience Scale-30 (personal vignette) showed that the higher self-efficacy group scored significantly higher on 25 of the 30 items, with small to medium effect sizes reported (see Table 5 ). This level of analysis highlights specific examples of responses to adversity where self-efficacious students responded in a more adaptive manner, providing a basis to better understand the precise nature of the influence of self-efficacy on resilience and offering a potential basis for interventions promoting resilience. Conversely, the items where there was no significant difference between self-efficacy groups are of little value in differentiating resilient and non-resilient students, at least on the basis of ASE beliefs. Responses to these items could still be adaptive or non-adaptive, conferring resilience or lack of it, but may be determined by individual difference constructs or processes other than self-efficacy. Similar analysis of responses to the vicarious adversity vignette revealed significant differences in only 4 of the 30 items, all with small effect sizes. This further supports the nature of self-efficacy as a mechanism for personal (human) agency and illustrates the limited influence of self-efficacy beliefs on the potential to perform academic advocacy roles, such as peer assisted learning mentors.

Results comparing responses to personal and vicarious vignettes revealed a significant difference and large effect size, with students reporting significantly higher resilience for the vicarious adversity vignette (see Figure 2 ). This effect was not explained by group differences in self-efficacy. That students advocate more positive adaptive responses to adversity experienced by a peer provides potentially valuable insights for resilience building. In general terms, it supports the value of peer mentoring and peer assisted learning and lessens concerns that negative belief systems might impact negatively on academic advocacy. In fact results suggest that students, including those with lower self-efficacy, are likely to be a positive source of encouragement and resilience for peers who are experiencing challenge and adversity. This is an important finding given continued growth in the implementation, evaluation and reputed benefits of peer assisted learning initiatives ( Ginsburg-Block et al., 2006 ; Smith et al., 2007 ; Romito, 2014 ). In more specific terms, results suggest that students are aware of what are and are not adaptive responses and have the potential to exhibit greater personal resilience than they may be currently exhibiting. One aspect of interventions promoting resilience could involve highlighting this difference between personal and vicarious resilience and encouraging students to reflect on their own reasons for advocating greater resilience for their peers and to explore the potential to move toward greater personal adoption of the responses advocated for their peers. Using examples of differences in specific responses, where significant differences in 23 of the 30 items are reported (see Table 7 ), could be helpful in this respect, enabling students to focus on areas where responses could be more adaptive.

Whilst academic resilience was significantly higher for the vicarious vignette for both lower and higher self-efficacy groups, the difference between personal and vicarious vignettes was greatest for lower self-efficacy students (see Figure 1 ). One interpretation of this is that lower ASE students have more to gain than students with higher self-efficacy from reflecting on how they respond to adversity experienced by a peer and using this to help promote more adaptive responses to personal adversity.

Consistent with previous studies ( Munro and Pooley, 2009 ; McLafferty et al., 2012 ), no significant differences in academic resilience according to age, gender, or year of study were observed in the present study. That females were heavily underrepresented in the sample does limit confidence in this particular finding, particularly in light of studies that do report greater academic resilience in female undergraduates (e.g., Allan et al., 2014 ).

Limitations

Although the study offers advances in applied academic resilience research and practice, some important limitations need to be considered when interpreting the results and conclusions of the study. Resilience studies commonly operationalize adversity in terms of difficult or unpleasant situations or experiences. It is suggested that the case vignettes developed for the study represent adversity in a relevant and authentic way for the purposes of studying academic adversity. Others—Martin and Marsh (2008 , 2009) and Martin (2013) for example—may argue that the vignette is not sufficiently traumatic, stressful or prolonged to adequately represent adversity as it is routinely represented in resilience studies. The ARS-30 is a newly developed measure of academic resilience and although findings do support its reliability and validity, further development work, particularly examining its predictive validity, will add to its integrity as a measure of academic resilience. Comparisons of personal and vicarious resilience were made between subject groups, introducing individual difference error; within-subject comparisons would provide a more robust basis upon which to draw conclusions regarding this aspect of the study. Also, given the differences that emerged between responses to the personal and vicarious case vignettes, those parts of the analysis that combine resilience response data across the vignettes should be treated with caution, focussing instead on analyses presented for the vignettes independently.

Future Directions

Whilst the lack of consensus that exists in terms of conceptualizing and operationalizing resilience ( Maclean, 2004 ; Friedland, 2005 ) is less pronounced within the narrower field of academic resilience (see Dweck, 2010 ; Duckworth, 2013 ; Martin, 2013 ), it is nonetheless suggested that there are two key areas of development necessary for increased impact of future general and academic resilience research. The first should address how best to capture aspects of resilience in a valid and reliable construct measure or measures. Grotberg (1997) for example summarizes the three aspects of resilience as: “I have” (e.g., trusting and loving relationships, encouragement to be independent); “I am” (e.g., proud of myself, responsible, hopeful); and “I can” (e.g., manage my feelings, solve problems). Similarly, caring relationships, good problem solving and intellectual functioning are identified by Masten and Coatsworth (1998) as factors promoting competency in individuals faced with adversity. The second area of development should continue to address the issue of identifying key factors and constructs associated with resilience. Discussing building resilience in vulnerable and disadvantage children and young people, Maclean (2004) identifies several familiar “qualities” or factors associated with resilience. These include initiative and insight, optimism, intellectual ability, placid temperament, trust, autonomy and decision making, humor, identity, social support, education, attainment, self-esteem and self-efficacy. Maclean goes on to raise the issue of the lack clarity surrounding how practioners can help individuals become more resilient; identifying associated constructs, as Duckworth's (2013) and Dweck's (2006 , 2010 ) have done with their constructs of grit and mindset, will assist the development and implementation of interventions promoting resilience, both in general and academic contexts. Evaluating new interventions is clearly a further avenue for research exploring academic resilience. Other avenues include longitudinal cohort studies examining the predictive value of academic resilience against outcomes including achievement, student satisfaction, retention and wellbeing.

In light of a recent impetus for intrapersonal research in education (Network on Intrapersonal Research in Education, 2015 ), future studies should consider examining both inter-individual and intra-individual variation in academic resilience. Such studies would reveal the extent to which population data can be generalized to patterns of resilience observed in individual students (and vice-versa), and would be particularly valuable in helping explore process aspects of resilience, as opposed to outcomes measures such as grade point average, in the evaluation of interventions or where adverse situations occur and are time-bound. Windle et al.'s (2011) description of resilience as the process of negotiating, managing and adapting to significant sources of stress or trauma emphasizes the importance of adopting such a process-focused view of resilience.

Conclusions

The present study sought to identify factors that contribute, in a meaningful way, to academic resilience and to examine how such factors influence specific, and meaningful, responses to academic adversity. Consistent with previous studies ( Hamill, 2003 ; Martin and Marsh, 2006 ), findings presented support ASE as predictive of academic resilience and go beyond earlier studies in identifying specific examples of responses to academic adversity, where lower and higher self-efficacy students respond in a differentially adaptive manner. As such, it is suggested that self-efficacy training, already shown to be effective in an educational context ( Siegle and McCoach, 2007 ), offers one approach to building academic resilience in students. Illustrating how self-efficacy influences specific responses to adversity, and the propensity to advocate greater resilience for peers facing adversity, should form another—metacognitive—aspect of resilience building for students. As Martin and Marsh (2006) have stated, identifying the specific facets comprising academic resilience will support an enhanced and more targeted approach to interventions aimed at enabling students to cope with the demands of academic life.

Conflict of Interest Statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: resilience, self-efficacy, adversity, student, learning

Citation: Cassidy S (2015) Resilience Building in Students: The Role of Academic Self-Efficacy. Front. Psychol . 6:1781. doi: 10.3389/fpsyg.2015.01781

Received: 29 May 2015; Accepted: 05 November 2015; Published: 27 November 2015.

Reviewed by:

Copyright © 2015 Cassidy. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Simon Cassidy, [email protected]

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Resilience Theory: A Summary of the Research (+PDF)

Resilience theory argues that it’s not the nature of adversity that is most important, but how we deal with it.

When we face adversity, misfortune, or frustration, resilience helps us bounce back. It helps us survive, recover, and even thrive in the face and wake of misfortune, but that’s not all there is to it.

Read on to learn about resilience theory in a little more depth, including its relationship with shame, organizations, and more.

But first, we thought you might like to download our three Resilience Exercises for free . These engaging, science-based exercises will help you to effectively deal with difficult circumstances and give you the tools to improve the resilience of your clients, students or employees.

This Article Contains:

What is resilience theory.

• 6 Impactful Articles on Resilience and Mental Toughness

What Research in Positive Psychology Shows

Resilience theory in social work, family resilience theory, shame resilience theory, community resilience theory, organizational resilience theory, the ‘science of resilience’, norman garmezy’s main findings and contribution, seligman’s 3ps model of resilience, a take-home message.

Resilience has been defined in numerous ways.

Defining resilience

The following definitions abound:

“the ability to bounce back from adversity, frustration, and misfortune”

Ledesma, 2014, p.1

“the developable capacity to rebound or bounce back from adversity, conflict, and failure or even positive events, progress, and increased responsibility”

Luthans, 2002a, p. 702

“a stable trajectory of healthy functioning after a highly adverse event”

Bonanno, 2004; Bonanno, Westphal, & Mancini, 2011

“the capacity of a dynamic system to adapt successfully”

Masten, 2014; Southwick, Bonanno, Masten, Panter-Brick, & Yehuda, 2014

When a panel discussion asked researchers to debate the nature of resilience , all agreed that resilience is complex. As a construct, it can have a different meaning between people, companies, cultures, and society. They also agreed that people could be more resilient at one point in their lives and less during another, and that they may be more resilient in some aspects of their lives than others (Southwick et al., 2014).

In case you’re interested, the table below from Greene, Galambos, and Lee (2004) shows even more ways resilience has been described.

Resilience theory

Resilience as a concept is not necessarily straightforward, and there are many operational definitions in existence. Resilience theory, according to van Breda (2018, p. 1), is the study of the things that make this phenomenon whole:

Its definition; What ‘adversity’ and ‘outcomes’ actually mean, and; The scope and nature of resilience processes.

6 Impactful Resilience Articles on Resilience and Mental Toughness

Ready to learn a bit more about resilience theory? For those who are keen to dig into the literature, this list demonstrates precisely how widely the concept can be applied: in social work, organizations, childhood development contexts, and more. You’ll find the full citations for these papers in the Reference section at the end of this article.

1. A Critical Review of Resilience Theory and Its Relevance for Social Work

In this literature review, Adrian van Breda (2018) considers peer-reviewed articles on resilience in the field of social work, discussing the evolution of an (as-yet to be established) consensus on its definition. He considers how it works and developments in the theory, looking at the study of resilience in South African cultures and societies.

2. Resilience Theory and Research on Children and Families: Past, Present, and Promise

Masten is known for her work on resilience and its role in helping families and children deal with adversity . In this article, she defines resilience as “the capacity of a system to adapt successfully to significant challenges that threaten its function, viability, or development” (Masten, 2018, p. 1).

Masten delves into the theory’s history and its research in this field in an attempt to integrate applications, models, and knowledge that may help children and their families grow and adjust.

3. Family Resilience: A Developmental Systems Framework

Professor Froma Walsh, cofounder of the Chicago Center for Family Health, has written extensively on family resilience and the positive adaptation of family units. In Family Resilience: A Developmental Systems Framework , Walsh (2016) considers the key processes in family resilience and gives a great overview of the concept from a family systems perspective.

4. Community Resilience: Toward an Integrated Approach

Berkes and Ross (2013) examined two distinct approaches to understanding community resilience: a social-ecological approach and a mental health and developmental psychology perspective. This article, which we unpack a little more further on, is a great read for anyone with an academic interest in the growing research on resilience at the community level.

5. Organizational Resilience: Towards a Theory and Research Agenda

Vogus and Sutcliffe (2007) attempted to define organizational resilience and examine its underpinning mechanisms. Their paper considers the relational, cognitive, structural, and affective elements of the construct before proposing some research questions for those with an academic interest in the topic.

6. Are Adolescents With High Mental Toughness Levels More Resilient Against Stress?

Download 3 Free Resilience Exercises (PDF)

These detailed, science-based exercises will equip you or your clients to recover from personal challenges and turn setbacks into opportunities for growth.

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Resilience and positive psychology are often closely related. Both are concerned with how promotive factors work, and both look at how a beneficial construct can facilitate our wellbeing (Luthar, Lyman, & Crossman, 2014).

Resilience theory and positive psychology are both applied fields of study, meaning that we can use them in daily life to benefit humanity, and both are very closely concentrated on the importance of social relationships (Luthar, 2006; Csikszentmihalyi & Nakamura, 2011).

So let’s look at what positive psychology research shows on resilience.

Character strengths and resilience

Strengths such as gratitude, kindness, hope, and bravery have been shown to act as protective factors against life’s adversities, helping us adapt positively and cope with difficulties such as physical and mental illness (Fletcher & Sarkar, 2013).

Some character strengths can also be significant predictors of resilience, with particular correlations between resilience and emotional, intellectual, and restraint-related strengths (Martínez-Martí & Ruch, 2017).

In their 2017 study, Martínez-Martí and Ruch found that hope, bravery, and zest had the most extensive relationship with positive adaptation in the face of challenge. This led the researchers to speculate that processes such as determination, social connectedness, emotional regulation , and more were at play.

From this particular cross-sectional study, however, no causal relationship was determined. In other words, we don’t know whether resilience impacts our strengths or vice versa.

The effect may work the other way around with adversity, and post-traumatic growth helps us build character strengths, but nonetheless, it’s an example of resilience and positive psychology’s interconnection (Tedeschi & Calhoun, 1995; Peterson, Park, Pole, D’Andrea, & Seligman, 2008).

Resilience and positive emotions

Most people think of happiness whenever positive psychology is mentioned, so are happiness and resilience related? Cohn, Fredrickson, Brown, Mikels, and Conway (2009) suggested that they may well be. To be specific, happiness is a positive emotion.

According to the broaden-and-build theory of positive emotions, happiness is one emotion that helps us become more explorative and adaptable in our thoughts and behaviors. We create enduring resources that help us live well (Fredrickson, 2004).

Cohn et al. (2009) found that participants who frequently experienced positive emotions such as happiness grew more satisfied with their lives by creating resources, such as ego resilience, that helped them tackle a wide variety of challenges.

These results correspond with other evidence that positive emotions can facilitate resource growth and findings that link psychological resilience with physical health, psychological wellbeing, and positive affect (Lyubomirsky, King, & Diener, 2005; Nath & Pradhan, 2012).

Its role in positive organizational behavior

Other studies have looked at resilience as one of numerous coping positive psychological resources, alongside optimism and hope.

Positive organizational behavior has been defined by Luthans (2002b, p. 59) as “the study and application of positively oriented human resource strengths and psychological capacities that can be measured, developed, and effectively managed for performance improvement in today’s workplace.”

Can training employees help encourage positive organizational behavior? The jury is still out (Robertson, Cooper, Sarkar, & Curran, 2015).

Some of the reasons for this are the central role of community relationships to both academic fields and the key social work principle that people should accept responsibility for one another’s wellbeing (International Federation of Social Workers, 2014).

One of the main drivers for more resilience theory research in social work contexts is the idea that identifying resilience-building factors can help at-risk clients in the following ways (Greene et al., 2004):

Promoting their competence and improving their health Helping them overcome adversity and navigate life stressors Boosting their ability to grow and survive

Concerning social workers, key issues in the field include:

Identifying protective factors and using them to inform interventions Using practical applications to promote the capacity and strength of individual clients, societies, and communities Understanding how social work policy and services promote or hinder wellbeing and social and economic injustice

Social work strategies for building client resilience

Greene et al.’s (2004) research also investigated the strategies and skills social workers relied on to boost the resilience of their clients. Some of these included:

Providing clients with safety and necessities when faced with adversity or traumatic events; for example, talking calmly with distressed individuals, reassuring them of their capabilities and ability to get through their troubles.

Listening, being present and honest, and learning from individuals’ stories while acknowledging their pain.

Promoting interpersonal relationships, attachments, and connections between people in a community or society.

Encouraging them to view themselves as a valued member of society.

Modeling resilient behaviors, such as dealing with work stress in healthy ways.

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This masterclass, based on scientific techniques, will provide you with all the material you need to deliver exceptional resilience training sessions. It is the ultimate shortcut to help others become more resilient. For more information, view our Realizing Resilience Masterclass page.

Shame resilience – Noor Pinna

Family resilience has been defined in several ways. One way of viewing the construct is as the “characteristics, dimensions, and properties of families which help families to be resistant to disruption in the face of change and adaptive in the face of crisis situations’’ (McCubbin & McCubbin, 1988, p. 247).

Another more recent definition describes it as the “capacity of the family, as a functional system, to withstand and rebound from stressful life challenges – emerging strengthened and more resourceful’’ (Walsh, 1996; 2003; 2016).

Both of these definitions take the concept of individual psychological or emotional resilience and apply it at a broader level; one of the key areas that interests researchers is how families respond immediately when faced with challenges and over the longer term (Walsh, 2016).

Family resilience processes

In a meta-analysis on family resilience, Walsh (2003) proposed that the concept involves nine dynamic processes that interact with one another and help families strengthen their ties while developing more resources and competencies.

• Making sense of adversity – e.g., normalizing distress and contextualizing it, viewing crises as manageable and meaningful
• Having a positive outlook – e.g., focusing on potential, having hope and optimism
• Spirituality and transcendence – e.g., growing positively from adversity and connecting with larger values
• Flexibility – e.g., reorganizing and restabilizing to provide predictability and continuity
• Connectedness – e.g., providing each other with mutual support and committing to one another
• Mobilizing economic and social resources – e.g., creating financial security and seeking support from the community at large
• Clarity – e.g., providing one another with information and consistent messages
• Sharing emotions openly – including positive and painful feelings
• Solving problems collaboratively – e.g., through joint decision-making, a goal-focus, and building on successes

The theory attempts to study how we respond to and defeat shame, an emotion we all experience. Brown (2008) describes shame resilience theory as the ability to recognize this negative emotion when we feel it and overcome it constructively in such a way that we can “retain our authenticity and grow from our experiences.”

Read more about shame resilience theory in this excellent article: Shame Resilience Theory : How to Respond to Feelings of Shame .

A community resilience concept

Magis (2010, p. 401) defined community resilience as the ”existence, development and engagement of community resources by community members to thrive in an environment characterized by change, uncertainty, unpredictability, and surprise.”

In other words, one approach to defining community resilience emphasizes the importance of individual mental health and personal development on a social system’s capacity to unite and collaborate toward a shared goal or objective (Berkes & Ross, 2013).

The key focus of community resilience is on identifying and developing both individual and community strengths and establishing the processes that underpin resilience-promoting factors (Buikstra et al., 2010). Its goals also include understanding how communities leverage these strengths together to facilitate self-organization and agency, which then contributes to a collective process of overcoming challenges and adversity (Berkes & Ross, 2013).

Community resilience is considered an ongoing process of personal development in dealing with adversity through adaptation and understandably plays a vital role in social work contexts (Almedom, Tesfamichael, Mohammed, Mascie-Taylor, & Alemu, 2007).

Relevant research questions related to community resilience theory include (Berkes & Ross, 2013):

• What are the characteristics of individual and community resilience, and how can these be fostered (Buikstra et al., 2010)?
• How is community resilience related to health, and how are health professionals able to help (Kulig, 2000; Kulig, Edge, & Joyce, 2008; Kulig, Hegney, & Edge, 2010)?
• How can community resilience improve readiness for disaster (Norris, Stevens, Pfefferbaum, Wyche, & Pfefferbaum, 2008)?

Community strengths promoting resilience

While community strengths vary between groups, Berkes and Ross (2013) identified a few characteristics that have a central role in helping communities develop resilience. These strengths, processes, and attributes include:

• Social networks and support
• Early experience
• People–place connections
• Engaged governance
• Community problem-solving
• Ability to cope with divisions

Just as people can develop their resilience, organizations can learn to rebound from and adapt after facing challenges. Organizational resilience can be thought of as “a ‘culture of resilience,’ which manifests itself as a form of ‘psychological immunity’” to incremental and transformational changes, according to Boston Consulting Group Fellow Dr. George Stalk, Jr. (Everly, 2011).

With a host of factors contributing to a dynamic and sometimes turbulent business environment, organizational resilience has gained incredible salience in recent years. And at the heart of it, Everly argues, are optimism and perceived self-efficacy.

How to build organizational resilience

A culture of organizational resilience relies heavily on role-modeling behaviors. Even a few credible and high-profile individuals in a company demonstrating resilient behaviors may encourage others to do the same (Everly, 2011).

These behaviors include:

• Persisting in the face of adversity
• Putting effort into dealing with challenges
• Practicing and demonstrating self-aiding thought patterns
• Providing support to and mentoring others
• Leading with integrity
• Practicing open communication
• Showing decisiveness

Read more about Positive Organizations here.

InBrief: the science of resilience

Are some people born more resilient than others? Southwick and Charney (2012) discussed human biological responses to trauma and looked at a sample of high-risk individuals to understand why some are more able to cope even in the face of life-changing adversity.

They examined three samples of participants to investigate whether these individuals had a genetic predisposition toward being more resilient:

• Special Forces instructors
• Vietnam prisoners of war
• Individuals who had suffered considerable trauma

Southwick and Charney (2012) looked at the psychological factors of these individuals; their genetic factors; and their spiritual, social, and biological factors.

The results:

Risk and protective factors generally have additive and interactive effects… having multiple genetic, developmental, neurobiological, and/or psychosocial risk factors will increase allostatic load or stress vulnerability, whereas having and enhancing multiple protective factors will increase the likelihood of stress resilience.

Put succinctly, genetic factors do have an important influence on our responses to trauma and stress. The image below gives a good overview of their findings.

Source: Southwick & Charney, 2012, p. 81

In the article , mentioned in our References section, you can learn more about two key concepts that are central to resilience theory:

• Learned helplessness – where individuals believe they are incapable of changing or controlling their circumstances after repeatedly experiencing a stressful event
• Stress inoculation – whereby they can develop an “adaptive stress response and become more resilient than normal to the negative effects of future stressors” (Southwick & Charney, 2012, p. 80)

University of Minnesota developmental psychologist Norman Garmezy is one of the best-known contributors to resilience theory as we know it. His seminal work on resilience focused on how we could prevent mental illness through protective factors such as motivation, cognitive skills, social change, and personal ‘voice’ (Garmezy, 1992).

His pioneering work included the Project Competence Longitudinal Study (PCLS), which contributed operational definitions, frameworks, measures, and more to the study of competence and resilience. Started around 1974, the PCLS was developed to enable more structured and rigorous resilience research and look into protective buffers that help children overcome adversity (Masten & Tellegen, 2012).

One of its more impactful discoveries was that resilience is a dynamic construct that changes over time; another was the concept of developmental cascades, which describe how functioning in one domain can influence other levels of adaptive function.

If you’re curious to find out more about the work of Norman Garmezy, Masten and Tellegen’s (2012) paper is a great read: Resilience in Developmental Psychopathology: Contributions of the Project Competence Longitudinal Study .

17 Tools To Build Resilience and Coping Skills

Empower others with the skills to manage and learn from inevitable life challenges using these 17 Resilience & Coping Exercises [PDF] , so you can increase their ability to thrive.

Created by Experts. 100% Science-based.

The best-known positive psychology framework for resilience is Seligman’s 3Ps model.

These three Ps – personalization, pervasiveness, and permanence – refer to three emotional reactions that we tend to have to adversity. By addressing these three, often automatic, responses, we can build resilience and grow, developing our adaptability and learning to cope better with challenges.

Seligman’s (1990) 3Ps are:

Personalization – a cognitive distortion that’s best described as the internalization of problems or failure. When we hold ourselves accountable for bad things that happen, we put a lot of unnecessary blame on ourselves and make it harder to bounce back.

Pervasiveness – assuming negative situations spread across different areas of our life; for example, losing a contest and assuming that all is doom and gloom in general. By acknowledging that bad feelings don’t impact every life domain, we can move forward toward a better life.

Permanence – believing that bad experiences or events last forever, rather than being transient or one-off events. Permanence prevents us from putting effort into improving our situation, often making us feel overwhelmed and as though we can’t recover.

These three perspectives help us understand how our thoughts, mindset, and beliefs affect our experiences. By recognizing their role in our ability to adapt positively, we can start becoming more resilient and learn to bounce back from life’s challenges.

Resilience is something we can all develop, whether we want to grow as individuals, as a family, or as a society more broadly. If you’re interested in developing your psychological resilience, our Realizing Resilience Masterclass uses science-based tools and techniques to help you understand the concept better and cultivate more “bounce-back.”

Or, if you’re hoping to read more about the topic in general, we’ve got a vast range of blog posts, worksheets, and activities in our Resilience & Coping section on this site. Before you go, though, tell us, what interests you most about resilience theory and what fields have you been applying it in professionally?

We hope you enjoyed reading this article. Don’t forget to download our three Resilience Exercises for free .

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This article seems very interesting and explains a lot of theory. For my PhD, I am seeking a suggestion regarding which theory or model would be particularly fit for a topic that addresses individual and organisational resilience to adapt in a disrupted labor market.

intresting PhD topic! Here are a few suggestions: – Resilience Theory : Explores how individuals and organizations withstand and adapt to adversity, offering insights into bouncing back from labor market challenges. – Psychological Capital (PsyCap) Theory : Investigates the role of an individual’s positive psychological state (hope, efficacy, resilience, optimism) in fostering adaptability and resilience.

I hope this helps and all the best with your research 🙂 Warm regards, Julia | Community Manager

Very good and interesting………….

This is a terrific summary of a complex area. Connect with me on LinkedIn please – I’m writing in this field also.

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The International Journal of Indian Psychȯlogy

Social Support & Psychological Well Being among Female Partners of Armed Forces & Non-Armed Forces

| Published: May 26, 2024

This research paper investigates the association between social support and psychological well-being among female partners, comparing those affiliated with armed forces to those in civilian settings. Recognizing the pivotal role of social support in fostering resilience and mental health, the study aims to uncover nuanced dynamics influencing the psychological experiences of these women. Drawing on established literature highlighting the positive impact of social support on well-being, the research employs a comparative approach to examine differences between female partners of armed and non-armed forces. A purposive sample of 100 married females, aged 25-35, equally divided between military and civilian backgrounds, participated in the study. Data collection utilized validated measures including the Multidimensional Scale of Perceived Social Support and Ryff’s Psychological Well-Being Scales. Statistical analyses, including t-tests, were conducted to explore the significance of social support and its impact on psychological well-being. Findings reveal that female partners of armed forces exhibit higher levels of psychological well-being compared to their civilian counterparts. Furthermore, the study demonstrates a significant positive association between social support and psychological well-being among female partners of armed forces, underscoring the importance of support systems in fostering resilience. The study contributes valuable insights into understanding the interplay between social support and psychological well-being in female partners of armed and non-armed forces. These findings carry implications for the development of tailored support interventions aimed at enhancing mental health and resilience among women in military and civilian communities.

Social Support , Psychological Well Being , Armed Forces , Non-Armed Forces

This is an Open Access Research distributed under the terms of the Creative Commons Attribution License (www.creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any Medium, provided the original work is properly cited.

© 2024, Yadav, A. & Gupta, C.

Received: April 10, 2024; Revision Received: May 22, 2024; Accepted: May 26, 2024

Anshika Yadav @ [email protected]

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Published in   Volume 12, Issue 2, April-June, 2024

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The mediating effect of psychological resilience between social support and anxiety/depression in people living with HIV/AIDS–a study from China

Yongbing sun.

The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China

Tianjun Jiang

Associated data.

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Objective To understand the relationship between psychological resilience in social support and anxiety/depression in people living with HIV/AIDS and to verify whether there is a mediating effect. Methods The questionnaire was administered to 161 people living with HIV/AIDS in a hospital. The questionnaire contained a general questionnaire, the Hospital Anxiety and Depression Scale (HADS), the Psychological Resilience Inventory (CD-RICS), and the Social Collaborative Support Scale (PSSS), and Pearson correlation analyses were used to explore the correlation between the factors and anxiety/depression, stratified linear regression analyses were used to validate the mediation model, and the bootstrap method was used to test for mediating effects. Results Anxiety was negatively correlated with psychological resilience and social support (r=-0.232, P < 0.01; r=-0.293, P < 0.01); depression was negatively correlated with psychological resilience and social support (r=-0.382, P < 0.01; r=-0.482, P < 0.01); there was a mediation effect model of social support between psychological resilience and anxiety/depression; psychological resilience played a fully mediating role in social support and anxiety/depression, with an effect contribution of 68.42%/59.34% and a 95% CI(-0.256~-0.036)/(-0.341 to~-0.106). Conclusion Psychological resilience plays a complete mediating effect between social support and anxiety/depression. It is recommended that more channels of social support be provided to patients with HIV/AIDS, thereby enhancing their psychological resilience and reducing anxiety/depression levels.

Introduction

HIV continues to be a major global public health issue, having claimed 40.4 million lives so far. In 2022, 630 000 people died from HIV-related causes globally. There were approximately 39.0 million people living with HIV at the end of 2022 with 1.3 million people becoming newly infected with HIV in 2022 globally [ 1 ]. In order to end the AIDS epidemic, the Joint United Nations Programme on HIV/AIDS (UNAIDS) has put forward the vision of “ending HIV infection by 2030”, and China has also promulgated the “Thirteenth Five-Year Plan of Action for Containing and Preventing AIDS in China” and the “Implementation Plan for Containing the Spread of AIDS (2019–2022)” [ 2 – 4 ].

With the discovery of epidemiological investigations, there is a significant phenomenon of psychological distress in people living with HIV/AIDS(PLWHA ), especially the state of anxiety and depression [ 5 ]. Thus, it is essential to study the psychological characteristics of PLWHA. It was found that the prevalence of depression among PLWHA was 22-44% [ 6 , 7 ]. While the prevalence of anxiety is 19% [ 8 ]. These anxiety and depression problems can affect the effectiveness of antiretroviral therapy and adherence, and increase the transmission and spread of HIV [ 9 ]. Consequently, reducing the level of anxiety and depression in PLWHA has been the focus of many researchers. In these studies, it has been established that both psychological resilience and social support are strongly correlated with anxiety/depression, respectively [ 10 – 12 ].

In addition, more in-depth studies have shown that increased levels of social support and psychological resilience can reduce levels of anxiety/depression [ 13 , 14 ]. At the same time, we found that there is a wider range of studies examining the mental health of PLWHA, and few studies have covered the relationship between psychological resilience and social support with anxiety/depression. We also did not find any similar studies that describe psychological resilience, social support and how they affect anxiety/depression in PLWHA. Therefore, we hope that this study will provide a more in-depth understanding of the psychological world of PLWHA and seek to understand the role and connection between psychological resilience and social support. To be able to provide evidence for a more refined study of the psychological world of PLWHA.

Study design and sample

We are using a cross-sectional research methodology and surveying a specific hospital in Beijing, the capital city of China. In China, information about HIV-infected patients is uploaded to the database of the Chinese CDC, and only specific hospitals are able to receive these HIV-infected patients and administer tests or treatments to them. And it is very appropriate to collect research data in such a specific hospital.

HIV/AIDS patients who attended the HIV outpatient clinic of a hospital in Beijing from January 2023 to August 2023 were selected for the study. Inclusion criteria: (1) Including HIV positive reports; (2) Includes normal cognition, understanding of the study and voluntary participation in cooperating to complete the questionnaire. Exclusion criteria: (1) Including those with significant cognitive impairment or impaired consciousness who could not cooperate in completing the questionnaire; (2) Those who did not want to cooperate in completing the questionnaire for personal reasons.

Data collection

A standardized-trained psychotherapist from the hospital outpatient clinic introduced the purpose of the study, the principle of confidentiality and related requirements to the patients, and instructed the patients to fill in the questionnaire on a one-to-one basis in strict accordance with standardized procedures. The questionnaire containing the General questionnaire, The Hospital Anxiety and Depression Scale, The psychological resilience scale and The Perceived social support scale was used to collect relevant information. The general questionnaire included demographic characteristics, such as age, education, income, number of sexual partners and occupation.

The Hospital Anxiety and Depression Scale (HADS) was used to measure patients’ anxiety and depression levels. The scale contains 7 questions on the anxiety subscale and 7 questions on the depression subscale, for a total of 14 questions, with a score of 1–4 for each question, and a score of more than 8 for each subscale indicates an abnormality, with higher scores indicating a more pronounced abnormality [ 15 ]. The Cronbach’s α coefficients of its total scale, anxiety subscale and depression subscale were 0.879, 0.806, 0.806 respectively, with good reliability and validity [ 16 ].

The psychological resilience scale (Connor-Davidson resilience scale, CD-RICS) was used to measure the level of psychological resilience of the patients. The scale contains 13 questions on the resilience subscale, 8 questions on the strength subscale, and 4 questions on the optimism subscale, for a total of 25 questions. Each question is scored 0–4, with higher scores indicating better psychological resilience. Its Cronbach’s alpha coefficient was 0.91, with good reliability and validity [ 17 ].

The Perceived social support scale (PSSS) was used to measure the level of social support of patients. The scale contains 4 questions on the family subscale, 4 questions on the friends subscale, and 4 questions on the other subscales, for a total of 12 questions. Each topic is scored 1–7, with 12–36 being low support level, 37–60 being medium support level, and 61 or more being high support level. The Cronbach’s alpha coefficients of its total scale, family subscale, friends subscale, and other subscales were 0.840, 0.818, 0.820, and 0.813, respectively, with good reliability and validity [ 18 , 19 ].

Statistical analysis

This study complied with the World Medical Association’s Declaration of Helsinki’s Ethical Principles and Good Clinical Practice for medical research in humans and all applicable regulations.The clinical research protocol and informed consent form were both approved by the Ethics Committee of the Fifth Medical Centre, General Hospital of the Chinese PLA. The subjects recruited for the clinical trial voluntarily signed the informed consent form. (KY-2023-6-41-1).

Study participant enrollment and characteristics

The questionnaire was distributed to 170 HIV/AIDS patients, and 161 valid questionnaires were collected, with a recovery rate of 94.71%. The average score of the anxiety scale is 13.72 ± 4.10, and the average score of the depression scale is 15.99 ± 2.58 (Table  1 ).

Analysis of the correlation between psychological resilience, social support, and anxiety/depression

The correlation analysis results indicate that anxiety is negatively correlated with psychological resilience and social support (r=-0.232, P < 0.01; r=-0.293, P < 0.01). Depression is also negatively correlated with psychological resilience and social support (r=-0.382, P < 0.01; r=-0.482, P < 0.01) (Table  2 ).

Related analysis(r)

b:P>0.01

Testing the mediation effect model

Testing the hypothesis of the mediation effect model through layered regression.

A model with social support as the independent variable, psychological resilience as the mediator, and anxiety/depression as the dependent variable was developed (Fig.  1 ).

The mediating effect model of psychological resilience between social support and anxiety or depression

Stratified regression analyses were performed when anxiety and depression were the dependent variables, respectively, with control variables (age, income, education, and number of sexual partners) in the first stratum, social support as the independent variable in the second stratum, and mediator variables in the third stratum. The results showed that there was no multicollinearity with VIF>3. In anxiety/depression Eq. 2 (R 2  = 0.083, P < 0.05/R 2  = 0.156, P < 0.01), social support was a significant impediment to the level of anxiety/depression (95% CI:-0.135 ~ -0.017, P < 0.01/ 95% CI: -0.127 ~ − 0.056, P < 0.01). With the addition of the mediator variable in anxiety/depression Eq. 3 (R 2  = 0.120, P < 0.01/R 2  = 0.257, P < 0.01), psychological resilience was a significant impediment to higher levels of anxiety/depression (95% CI:-0.138 ~ -0.017, P < 0.05/95% CI: − 0.115 ~ -0.046, p < 0.01). The model hypotheses were valid and psychological resilience played a fully mediating role between social support factors and anxiety/depression ( Table  3 ).

Stratified regression analyses of anxiety/depression

Testing for mediating effects via bootstrap

The mediating effects were further tested using the bootstrap method, where the mediating effects model was tested using repeated random sampling 5000 times in the raw data. The results showed that the total effects of anxiety/depression were all significant, none of the direct effects were significant, and none of the confidence intervals for the indirect effects contained 0. Psychological resilience played a fully mediating role in social support factors and anxiety/depression, with an effect contribution of 68.42%/59.34% (Table  4 ).

Validation of the mediating role of social support between psychological resilience and anxiety/depression

Resilience refers to the act of coping, adapting, or thriving from adversity, and reflects a complex and dynamic interplay between individual, environmental, and sociocultural domain [ 20 ]. Social support is a social network consisting of three dimensions: family support, friend support, and other support ( such as social relationships with neighbors, leaders, etc.) [ 21 ].The level of social support reflects the extent to which an individual is linked to social relationships. The higher the level, the more closely the individual interacts in society [ 22 ].

The relationship between psychological resilience, social support, and anxiety/depression is very strong. From the results of this study to observe the relationship between these four factors, an increase in psychological resilience and social support can significantly reduce the level of anxiety/depression, respectively. This result is consistent with the findings of several international studies [ 23 – 25 ]. Among these studies, Leodoro J. Labrague et al.‘s study and Zhi Ye et al.‘s study, although introduced in both social support and psychological resilience can enhance mental health. However, the subjects were healthcare workers and university students, which is different from the target population of this study. While Aneela Hussain et al.‘s study targeted the HIV-infected population and described the importance of social support for mental health. However, none of these studies reported, described the role of psychological resilience in the middle of social support and anxiety/depression.

In the present study, we found that PLWHA who have better scores on psychological resilience and social support mean that they are better able to adapt to being HIV-infected and to survive in society or socialise as HIV-infected people. This adaptation to the environment reduces anxiety or depression due to discrimination or inconvenience of living with HIV infection [ 26 , 27 ].In looking at PLWHA, Frank H. Galvan concluded that social support is not only an important factor in influencing mental health in addition to the stigma of HIV, but further found a strong relationship between the friend dimension and HIV stigmatization [ 28 ].Meanwhile, Cierra N. HOPKINS et al. confirmed the important relationship between psychological resilience and mental health [ 29 ]. The results of these two studies are also consistent with some of the results of this study.

In further analysis, we constructed a model of the relationship between psychological resilience, social support, anxiety/depression. The results of the model revealed that social support can directly influence the level of anxiety as well as the level of depression in PLWHA. For a special group of HIV-infected people, the support of family and friends is extraordinarily important [ 30 ]. Especially the support of sexual peers. It can even be said that it can influence all aspects of PLWHA, such as how they deal with stigma, whether they take medication as required, and whether they engage in suicidal behaviour [ 31 – 33 ]. Therefore, based on the findings of the study, we suggest that the relevant authorities can pour more resources into family education and sexual peer education for PLWHA.

We found that psychological resilience mediated the effect of social support on anxiety levels or depression levels. Not only can the level of social support directly influence the level of anxiety or depression in PLWHA, but it can also influence the level of anxiety or depression through the strength of psychological resilience. In addition, psychological resilience is an important protective factor for people with low levels of social support and can reduce the occurrence of anxiety and depression [ 34 ]. Thus, having good psychological resilience can reduce the occurrence of anxiety or depression at the same level of social support. This provides a strong support in terms of mental health education for PLWHA.

Strengths and limitations

The results of this study, innovatively confirm, the mediating role of psychological resilience. It also proves how social support and psychological resilience influence anxiety/depression levels in PLWHA. It will tell us a way to a further, more refined understanding of the mental world of PLWHA.

Some limitations of this study that may affect our findings include the small sample size; the data collected may be biased. As the sample was only collected within a single hospital in Beijing, the generalisability of the results must be interpreted with caution. In addition, participant-reported data may have limited the results. Even though we took certain measures to maintain data integrity, it is still not possible to avoid participants’ self-reported data being over- or under-reported. Finally, one of the more unfortunate aspects is the low number of factors for demographic characteristics. This could potentially lead to a number of influencing factors being undetected.

This study determined the relationship between psychological resilience, social support, anxiety/depression. Social support reduces levels of anxiety or depression in HIV-infected individuals, as does psychological resilience. In addition, psychological resilience is an important mediator between social support and anxiety or depression. Greater psychological resilience prevents the experience of anxiety or depression due to low levels of social support, and mental health work with PLWHA can be more beneficial if it is undertaken in the context of both social support and psychological resilience. Therefore, based on the results of this study, we recommend increased investment in psychotherapy. Mental health judgement, family and peer education by psychotherapists for PLWHA may be a good option [ 35 – 37 ].

Acknowledgements

The authors would like to thank all participants for their participation in this study.

Author Contributions

Yongbing Sun’s job is to carry out the design of the entire study, promote the implementation of the study, analyze data, and obtain results, and write articles. The work of Bing Song and Juan Cheng is to assist in promoting research implementation. Cheng Zhen and Chao Zhang’s job is to participate in writing and translating articles. The work of Tianjun JIANG supervised the design, implementation, and writing of the entire study.All authors read and approved the final manuscript.

This study was funded by the [National Key R&D Plan].(2022YFC2305004).

Data Availability

Declarations.

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

The authors declare no competing interests.

Informed consent was obtained from all individual participants included in the study.

Not applicable.

Yongbing Sun and Bing Song are co-first authors.

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Yongbing Sun and Bing Song are contributed equally to this work.