Perceived stress as a mediator between religiosity and resilience among Malaysian undergraduates

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Grounded in Resilience Theory and Conservation of Resources Theory, this study examined the mediating role of perceived stress in the relationship between religiosity and resilience among undergraduate students. Methods This cross-sectional explanatory correlational study involved 119 undergraduates (51.3% female; Mean age = 20.90, SD = 1.31;) who completed the Santa Clara Strength of Religious Faith Questionnaire (SCSRFQ), Perceived Stress Scale (PSS-10), and Brief Resilience Scale (BRS) online via Google Forms. Mediation analysis was conducted using the PROCESS macro (Model 4) with 5,000 bootstrap samples and a 95% confidence interval. Results Analysis showed that perceived stress fully mediated the relationship between religiosity and resilience ( β = 0.026, 95% CI [0.011, 0.040]), accounting for 97.72% of the total effect. Religiosity significantly predicted lower perceived stress ( β =–0.336, p <.001), and in turn, perceived stress significantly predicted lower resilience ( β =–0.077, p <.001). Conclusion These findings suggest that religiosity enhances resilience not directly, but through its stress-buffering function. This study offers a theoretically integrated model and highlights stress reduction as a key mechanism for strengthening resilience among undergraduate students. perceived stress religiosity resilience mediation undergraduate students Figures Figure 1 1. Introduction Resilience, defined as the ability to recover from setbacks, adapt to change, and persevere through hardship, has emerged as a key component of mental health [1, 2]. It is a dynamic process that activates self-knowledge and self-regulation, thereby allowing individuals to adapt positively to challenges and stressors [3]. Resilience plays a significant role among undergraduate students by helping them regulate emotions, maintain control, and utilise both internal and external resources to overcome adversity [4]. It not only supports mental health and well-being [2] but also fosters positive emotions such as gratitude, enabling students to transform these feelings into sustained well-being and to thrive even in demanding environments [5]. Developing such resilience is a pressing concern, as undergraduate students are a demographic often subjected to substantial stress arising from academic pressures, social dynamics, and the transition into adulthood [6]. Despite numerous initiatives implemented in recent years to mitigate student stress, the overall effectiveness of these interventions remains unclear and inconclusive [7]. This issue is particularly pronounced in Malaysia, where one study reported an alarmingly high prevalence of stress among undergraduate students, with 62.3% experiencing significant stress levels [8]. A major challenge in addressing this widespread problem is the lack of adequate resources to meet the growing demand for support within educational institutions [9]. Furthermore, the shortage of mental health professionals and specialised services in Malaysia exacerbates the issue by creating financial barriers that hinder students from seeking professional help [10]. Prolonged exposure to unmitigated stress can lead to severe psychological consequences. According to the Conservation of Resources (COR) Theory, stress is a process involving the actual or threatened loss of valuable resources, leading to a state of depletion [11]. Individuals experiencing high stress levels often suffer from diminished cognitive and emotional resources, which can initiate loss spirals that undermine well-being [12]. This depletion is harmful to the psychological resources required for resilience [11] and significantly increases the risk of suicidal behaviour [13, 14]. Research highlights that suicide is a leading cause of death among undergraduate students [15]. Meta-analyses have identified a 14% prevalence of suicide-related outcomes within this demographic [16], while other studies report a 15.2% prevalence rate for suicidal ideation [17]. In Malaysia, this trend is particularly alarming, with high-risk suicidal behaviours reported between 20% and 23.8% [13, 14]. Given these challenges and the limited accessibility of psychological resources in Malaysia, it is critical to explore alternative protective factors that can buffer against stress. One such factor is religiosity, which has been shown to play a significant role in building resilience [18]. Religious teachings often provide a sense of meaning, purpose, social support, and coping mechanisms such as optimism and hope—qualities that align with and reinforce personal resilience [18]. Consistent with this, several Malaysian studies have found positive correlations between religiosity and resilience [19–21]. The relationship between religiosity and resilience is proposed to be indirect, operating through the mechanism of stress reduction. Resilience Theory offers a clear framework for this pathway. Religiosity can be conceptualised as a multidimensional construct that builds both internal assets (personal faith, meaning, and hope) and external resources (social support within religious communities) [18, 22–24]. Through these assets and resources, religiosity functions as a stress buffer [25]. This buffering occurs both externally—through social support [22–24]—and internally—through cognitive and emotional reframing provided by faith, such as Islamic Tawakkul (trust in divine will) [25], the mindful acceptance of suffering in Buddhism [26, 27], forgiveness in Christianity [28, 29], and the karmic understanding of causality in Hinduism [30]. This process is further explained by the Conservation of Resources Theory [11]. The assets and resources derived from religiosity are hypothesised to reduce perceived stress, thereby conserving individuals’ psychological capacity and preventing further depletion [12]. Reduced stress allows individuals to retain the fuel necessary to rebound and exhibit resilience [3, 31, 32]. Conversely, high perceived stress depletes limited cognitive and emotional resources, creating loss spirals that make individuals more vulnerable to future stressors [12]. This mechanism clarifies why resilience is resource-intensive, requiring focus, optimism, and energy to bounce back. When religiosity buffers perceived stress, these psychological reserves are conserved, providing the foundation for resilience to manifest. Empirical evidence consistently supports this inverse relationship, showing that lower perceived stress significantly predicts higher resilience [5, 16, 33]. Given that the literature supports the link between religiosity and reduced stress, as well as the association between lower stress and greater resilience, this study proposes a mediation model to explain the overall relationship between religiosity and resilience. Specifically, it is hypothesised that perceived stress mediates the relationship between religiosity and resilience, such that higher levels of religiosity lead to reduced perceived stress, which in turn enhances resilience among Malaysian undergraduate students. 1.2 Research Objectives Building upon the gaps identified in the literature and the researchers’ hypotheses, this study aims to investigate the following objectives among undergraduate students in Klang Valley: RO1: To examine the relationships between religiosity, perceived stress, and resilience. RO2: To explore the mediating effect of perceived stress on the relationship between religiosity and resilience after controlling for gender, religious affiliation and age. 2. Methods All methods were carried out in accordance with relevant guidelines and regulations. 2.1 Research Design and Setting This cross-sectional correlational explanatory study was conducted between September to November 2024 among university students in the Klang Valley, Malaysia. The Klang Valley hosts the largest cluster of reputable public and private universities and colleges, establishing it as the major hub for tertiary education in the country. 2.2 Participants Participants were recruited based on the following inclusion criteria: (1) Malaysian citizen currently enrolled in an undergraduate programme in the Klang Valley, and (2) individuals who self-identified as adhering to a religion. Exclusion criteria included individuals who identified as atheists, those unable to comprehend the survey instructions, and those currently undergoing medical treatment for stress-related disorders. G*Power software version 3.1 was used to determine the recommended sample size, using an a priori analysis, the F test family for linear multiple regression (fixed model, R 2 deviation from zero). Based on a small-to-medium effect size ( f 2 =0.09), derived from the smallest significant predictor, r =-.29 [34], an alpha level of .05, and a power of .80 for a model with two predictors, the required sample size was 89. Accounting for an anticipated attrition rate of 20%, a total of 107 participants were targeted in the study. 2.3 Study Procedure The convenience sampling strategy was used to recruit participants based on their accessibility [35]. Data were collected online using a Google Form that included the informed consent statement and the survey instruments. The link and QR code to the Google Form were disseminated via social media platforms such as Instagram, Facebook, and email. Upon providing consent, participants were directed to complete the survey questionnaires, which required approximately 10–15 minutes. This recruitment approach was cost-effective. All data were collected anonymously, and confidentiality was ensured throughout the process. 2.4 Instruments Background Information Participants’ basic demographic information, including age, gender, and religious affiliation, was collected. Perceived Stress Scale (PSS-10) Perceived stress was assessed using the 10-item Perceived Stress Scale (PSS-10) [36]. Participants rated each item on a 5-point Likert scale (0 = Never to 4 = Very Often) based on their feelings and thoughts over the past month. After reverse-scoring four items, all item scores were summed. Total scores range from 0 to 40, with higher scores indicating greater perceived stress. The PSS-10 has demonstrated good internal consistency (α=.82), test–retest reliability (α=.79) [38, 39], and construct validity [40]. In the present study, the scale demonstrated good internal consistency reliability (α=.85). Santa Clara Strength of Religious Faith Questionnaire (SCSRFQ) Religiosity was measured using the 10-item SCSRFQ [37]. This scale evaluates the strength of an individual's religious faith (e.g., "My religious faith is extremely important to me.") using a 4-point Likert scale (1 = strongly disagree to 4 = strongly agree). Items scores were summed, with higher scores representing stronger religious faith. The SCSRFQ has shown high internal consistency reliability ( α =.95) [38] and good validity [37]. In the present study, the scale showed excellent internal consistency reliability ( α =.94). Brief Resilience Scale (BRS) Resilience was measured using the 6-item Brief Resilience Scale (BRS) [39] which assesses an individual’s ability to bounce back from stress (e.g., "I tend to bounce back quickly after hard times."). Participants responded on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). After the reverse-scoring three items, the final score was calculated as the mean of all six items. Higher mean scores indicate greater resilience. The BRS has demonstrated good reliability ( α =.71) [40] and validity in Southeast Asian contexts [41]. In the present study, the scale reported acceptable internal consistency reliability ( α =.73). 2.5 Data Analysis All statistical analyses were conducted using IBM SPSS Statistics (Version 29.0). The sample’s background characteristics were analysed using descriptive statistics. The Pearson correlation coefficient was used to determine the strength and direction of the linear relationship between religiosity, perceived stress and resilience. To quantify the mediation effect, the PROCESS Macro (Model 4) developed by Hayes was employed [42]. In this model, religiosity was entered as the predictor, resilience as the outcome, and perceived stress as the mediator. A bootstrapping procedure with 5,000 samples and a 95% confidence interval was conducted to examine the indirect effects. The mediation (indirect) effect was considered statistically significant if the 95% bias-corrected confidence interval (CI) did not include zero. 3. Results and Discussion 3.1 Results A total of 119 participants (Mean=20.90 years, SD=1.31) took part in this study. Most participants were female (51.3%) and affiliated with Buddhism (45.4%). Independent samples t-tests showed no significant gender differences in religiosity, t (117)=0.434, p =.665, or perceived stress, t (117)=1.159, p =.249. Similarly, the Welch’s t-test indicated no significant gender difference in resilience, t (107.31)=0.022, p =.983 (Table 1). A one-way ANOVA examining differences across religious affiliation found a significant effect on religiosity, F (3, 115)=15.423, p <.001, but no significant differences for perceived stress, F (3, 115)=0.193, p =.901, or resilience, F (3, 115)=0.498, p =.684. Post hoc comparisons using the Bonferroni correction revealed that Muslim participants reported significantly higher religiosity compared with Buddhist ( p <.001), Christian ( p =.039), and Hindu ( p =.020) participants (Table 1). Table 2 presents the results of the Pearson correlation analyses. Age was positively associated with perceived stress ( r =.250, p <.01), but was inversely associated with resilience ( r =–.247, p <.01). Religiosity was negatively associated with perceived stress (r=–.290, p<.01) and positively associated with resilience ( r =.221, p <.05). Higher perceived stress was strongly associated with lower resilience ( r =–.762, p<.01). Table 2 Pearson correlation coefficients among study variables (N=119) Variable Mean ± SD 1. 2. 3. 4. Age 20.90 ± 1.31 - - Religiosity 23.55 ± 7.65 - .038 - Perceived Stress 19.66 ± 6.34 .250** -.290** - Resilience 3.16 ± 0.65 -.247** .221* -.762** - Note: ** p <.01, * p <.05 The mediation effect of perceived stress on the relationship between religiosity and resilience, controlling for gender, religious affiliation and age, was analysed using the SPSS PROCESS macro (Model 4) by Hayes [42]. As shown in Table 3, religiosity significantly predicted perceived stress ( β =–0.336, t =-4.016, p <.001), and perceived stress significantly predicted resilience ( β =–0.077, t =-11.167, p <.001). However, the direct effect of religiosity on resilience was not significant after controlling for perceived stress ( β =0.001, t =0.093, p =.926). The bootstrapped indirect effect was 0.026, with a 95% CI [0.011, 0.040]. Since the confidence interval did not include zero, the mediation effect was significant. These findings indicate that perceived stress fully mediates the relationship between religiosity and resilience, accounting for 97.72% of the total effects (Table 4). Figure 1 presents the mediation pathway model. Figure 1 illustrates the mediation pathway model. Table 3 Regression model of mediation analysis after controlling for covariates (N=119) Path Regression Model Goodness-of-Fit Indices Coefficient significance Outcome variable Predictor variable R R 2 F β t Path a Perceived stress Religiosity .445 .198 4.605 -.336 -4.016* Path c’ (direct) Path b Resilience Religiosity Perceived stress .774 .599 23.720 .001 -.077 0.093 -1.167* Total effect c Resilience Religiosity .386 .149 3.275 .026 2.990* Note: * p <.001 Table 4 Bootstrap analysis of multiple mediation effects Effect size SE Percentage of Total Effect 95% CI Lower Limit Upper Limit Total effect (c) .026 .009 .009 .044 Direct effect (c’) .001 .006 2.28% -.012 .013 Indirect effect (a*b) .026 .007 97.72% .011 .040 3.2 Discussion To the best of the authors’ knowledge, this study is the first to integrate Resilience Theory and Conservation of Resources (COR) Theory to examine the relationships between religiosity, perceived stress, and resilience among undergraduate students. The findings showed that religiosity was associated with lower perceived stress and higher resilience, while perceived stress was strongly and inversely related to resilience. Mediation analysis further demonstrated that perceived stress fully mediated the relationship between religiosity and resilience, whereby religiosity negatively predicted perceived stress, and perceived stress, in turn, negatively predicted resilience. The present study’s first significant pathway (Religiosity → Perceived Stress, path a) provides robust empirical support for Resilience Theory. Consistent with prior research [25, 29], findings indicate that religiosity acts as a vital buffer against stress. This aligns with Resilience Theory, which conceptualises religiosity as offering both internal assets (personal faith, meaning-making) and external resources (social support) [18, 22–24]. Externally, engagement in religious activities—such as prayer, festivals, or scriptural reading—fosters community interaction, acting as a stress-buffering social resource. Internally, religious doctrines, such as Buddhist mindfulness, equip individuals with cognitive assets to restructure their understanding of setbacks [30]. By specifically measuring on personal faith, this study enriches the “faith vs. social support” debate [30], suggesting that beyond social benefits, religion functions as a powerful cognitive mechanism for reappraising stress. Furthermore, within the framework of COR Theory, religiosity functions as a critical mechanism for maintaining cognitive and emotional capital, thereby helping individuals conserve or replenish their resource pool amidst stressful circumstances [12]. Consequently, individuals with higher religiosity perceive less stress, as they are better equipped with effective, religiously informed coping strategies to manage challenging circumstances. The second significant pathway (Perceived Stress → Resilience, path b) provides empirical support for COR Theory [11]. The theory posits that stress functions as a mechanism of resource depletion; high stress triggers a resource drain that exhausts the finite cognitive and emotional reserves necessary for adaptive coping. This finding confirms a strong inverse relationship between stress and resilience, aligning with prior studies [3, 33, 39, 43]. More specifically, elevated stress directly undermines an individual's capacity to mount a resilient response by depleting essential assets [12]. Conversely, when stress is reduced, this drain is curtailed, preserving the psychological “fuel” required to “bounce back” from adversity. Consequently, individuals retain greater mental energy to cultivate core resilient attributes—such as self-control, optimism, and mindfulness[12]. By conserving these cognitive resources, individuals are better positioned to build and maintain the fundamental characteristics of a resilient individual. Together, these findings explain the full mediation mechanism of perceived stress in the relationship between religiosity and resilience, which is aligned with past research [20, 21, 24]. A key insight is the theoretical parsimony of the model: religiosity, a relatively stable internal and external resource, is logically positioned to buffer perceived stress—a more transient psychological state [44, 45]. This supports an integrated interpretation of Resilience Theory, which views religiosity as both an internal asset and an external resource [18, 22–24], alongside COR Theory’s emphasis on the depletion and conservation of coping resources [11]. The findings suggest that religiosity contributes resources that function as stress buffers, conserving the psychological capacity needed to bounce back and enhance resilience [3, 33, 39, 43]. This also clarifies the non-significant direct effect of religiosity on resilience (path c’), indicating that the previously observed “total effect” primarily reflected an unobserved indirect pathway through stress. The primary insight of this mediation model is that religiosity does not enhance resilience directly. Instead, its influence is fully channelled through its role in reducing perceived stress—a more fundamental mechanism. This highlights perceived stress as the “active ingredient” linking religiosity to resilience. This has important implications for intervention design. It suggests that resilience is strengthened not by religiosity per se but through the stress-coping processes embedded within religious teachings and practices. Practically, individuals benefit from engaging in religious activities that reduce stress, rather than relying on the abstract belief that religiosity inherently builds resilience. An alternative explanation for the non-significant direct effect is that religious identity alone (as measured in this study) may be insufficient; active practice of one’s faith may be required to activate the stress-buffering mechanism. This remains an important avenue for future research. 3.3 Public Health Implications This mediation model offers significant public health implications, particularly for university populations facing a well-documented lack of accessible mental health resources [9, 10]. First, the model identifies stress reduction as the primary mechanism for building resilience. This highlights a powerful, preventive public health strategy, suggesting that institutions can foster resilience not only by treating students but also by implementing systemic stress-reduction policies. For universities, this implies that policies addressing student workload, examination schedules, and financial stressors are not merely administrative decisions but represent tangible mental health interventions. Moreover, this study provides empirical support for culturally embedded, non-medicalised mental health pathways. In a multicultural nation such as Malaysia, the findings suggest that strengthening existing and trusted community structures (e.g., Buddhist societies, Muslim student associations) can serve as a highly effective, low-cost, and feasible public health approach to supporting student well-being. These results may also offer religious student organisations insight into developing stress-reduction-focused programmes that indirectly enhance resilience. Finally, by demonstrating that faith-based practices play a significant role in the stress–resilience pathway, this research helps destigmatise help-seeking outside formal clinical settings. This is crucial, as it empowers students to utilise resources that are already accessible, culturally meaningful, and personally relevant to them. 3.4 Limitations and Future Research The generalisability of the findings is limited by the use of convenience sampling and the geographical restriction to the Klang Valley. These constraints reduce the ability to capture the Malaysia’s broader multiethnic and multi-faith undergraduate population. Future studies should adopt multistage or multicentre sampling to obtain a more representative range of religious affiliations. Additionally, the cross-sectional correlational design prevents any causal inferences. Longitudinal or experimental designs are recommended to more accurately establish the temporal and causal relationships among religiosity, perceived stress, and resilience. 3.6 Conclusion This study successfully clarified the complex relationship between religiosity, perceived stress, and resilience. It moves beyond simple correlations to demonstrate a clear mechanism: religiosity does not directly build resilience but enhances it by first reducing individual’s perceived stress. These insights are crucial for developing more effective, culturally sensitive, and resource-efficient mental health interventions for university students. Declarations All methods were carried out in accordance with relevant guidelines and regulations. Ethics Approval and Consent to Participate This study received approval from the Ethics Committee of Tunku Abdul Rahman University of Management and Technology (TAR UMT). Informed consent was obtained digitally from all participants prior to their participation. AI Usage Declaration Language editing assistance was provided using an AI-based writing tool to improve clarity and academic style. All content and interpretations remain the authors’ original work. Funding The authors received no financial support for the research, authorship, or publication of this article. Clinical trial number Not applicable. Data Availability The datasets generated and/or analysed during the current study are not publicly available due to participant privacy and ethical restrictions but are available from the corresponding author on reasonable request. Consent to Publish Not Applicable References VanMeter F, Cicchetti D. Chapter 7: Resilience. In: Gallagher A, Bulteau C, Cohen D, Michaud JL, editors. Handbook of Clinical Neurology. Elsevier; 2020. pp. 67–73. Egan LA, Park HR, Lam J, Gatt JM. Resilience to stress and adversity: A narrative review of the role of positive affect. Psychol Res Behav Manage. 2024;17:2011–38. https://doi.org/10.2147/PRBM.S391403 . Pilipenko TS. Self-acceptance as a personal resource of resiliency. Humanit Social Studies: Far East. 2020;17:40–4. https://doi.org/10.31079/1992-2868-2020-17-4-40-44 . 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The role of religion in buffering the impact of stressful life events on depressive symptoms in patients with depressive episodes or adjustment disorder. Int J Environ Res Public Health. 2019;16:1238. https://doi.org/10.3390/ijerph16071238 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 Apr, 2026 Reviews received at journal 23 Apr, 2026 Reviews received at journal 22 Mar, 2026 Reviewers agreed at journal 12 Mar, 2026 Reviewers agreed at journal 12 Mar, 2026 Reviewers invited by journal 09 Mar, 2026 Editor assigned by journal 09 Mar, 2026 Submission checks completed at journal 08 Mar, 2026 First submitted to journal 08 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8873152","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":605351364,"identity":"a1bab04a-cb72-4b7e-a286-459e2e138bdf","order_by":0,"name":"Yet Sern Teh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYHADHsYHIJKPsEpmqAYGHmYDEM1GihY2CRCDoBaD4/2HP3zcc4fBnr33WOXXHDsZNgbmh49u4NNy5jCb5Ixnzxh4eM6l3Zbdlgx0GJuxcQ4+LTeS2Zh5Dhxm4JHIMbstuY0ZqIWHTRqvlvuPmT//AWmRf2NWLLmtnggtN5gZpBnAtvCYMX7cdpiwFskzyWaSPQcO8/CcyTGWZtx2nIeNmYBf+I4ffPzhx4HDcuztZww//txWbc/P3vzwMT4tCgcgNA+IYIaQeJSDgHwDEofxBwHVo2AUjIJRMDIBACwcQ76fwOUZAAAAAElFTkSuQmCC","orcid":"","institution":"Tunku Abdul Rahman University of Management and Technology","correspondingAuthor":true,"prefix":"","firstName":"Yet","middleName":"Sern","lastName":"Teh","suffix":""},{"id":605351365,"identity":"5c983d5c-1ecf-4f3b-9651-c8cd840062fb","order_by":1,"name":"Kwai Ching Kan","email":"","orcid":"","institution":"Tunku Abdul Rahman University of Management and Technology","correspondingAuthor":false,"prefix":"","firstName":"Kwai","middleName":"Ching","lastName":"Kan","suffix":""},{"id":605351366,"identity":"0508e11e-74fb-4cd9-9f4e-ecce539d4279","order_by":2,"name":"Chee Seong Low","email":"","orcid":"","institution":"Tunku Abdul Rahman University of Management and Technology","correspondingAuthor":false,"prefix":"","firstName":"Chee","middleName":"Seong","lastName":"Low","suffix":""},{"id":605351367,"identity":"904ba918-a967-41a2-92c4-4bb580b313af","order_by":3,"name":"Aaron Jun Qi Ng","email":"","orcid":"","institution":"Tunku Abdul Rahman University of Management and Technology","correspondingAuthor":false,"prefix":"","firstName":"Aaron","middleName":"Jun Qi","lastName":"Ng","suffix":""}],"badges":[],"createdAt":"2026-02-13 14:53:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8873152/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8873152/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104557806,"identity":"de2d6822-0bd9-4bbb-8559-5b4bf75800f1","added_by":"auto","created_at":"2026-03-13 09:31:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69055,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMediation model of perceived stress mediating the relationship between religiosity and resilience.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCoefficients are unstandardised (Standard Error in parentheses). The coefficient \u003c/em\u003e\u003ccode\u003e\u003cem\u003ec\u003c/em\u003e\u003c/code\u003e\u003cem\u003e is the total effect; \u003c/em\u003e\u003ccode\u003e\u003cem\u003ec'\u003c/em\u003e\u003c/code\u003e\u003cem\u003e is the direct effect., ns = non-significant, \u003c/em\u003e*\u003cem\u003ep\u003c/em\u003e\u0026lt;.001\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8873152/v1/f5adb8a38a7e95b020140e96.png"},{"id":104781482,"identity":"32cf2774-dacb-4548-abc1-0aae08c1f66a","added_by":"auto","created_at":"2026-03-17 07:55:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":790852,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8873152/v1/7997884d-d037-4a67-8a1f-00c4b291f5ab.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Perceived stress as a mediator between religiosity and resilience among Malaysian undergraduates","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eResilience, defined as the ability to recover from setbacks, adapt to change, and persevere through hardship, has emerged as a key component of mental health [1, 2]. It is a dynamic process that activates self-knowledge and self-regulation, thereby allowing individuals to adapt positively to challenges and stressors [3]. Resilience plays a significant role among undergraduate students by helping them regulate emotions, maintain control, and utilise both internal and external resources to overcome adversity [4]. It not only supports mental health and well-being [2] but also fosters positive emotions such as gratitude, enabling students to transform these feelings into sustained well-being and to thrive even in demanding environments [5].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDeveloping such resilience is a pressing concern, as undergraduate students are a demographic often subjected to substantial stress arising from academic pressures, social dynamics, and the transition into adulthood [6]. Despite numerous initiatives implemented in recent years to mitigate student stress, the overall effectiveness of these interventions remains unclear and inconclusive [7]. This issue is particularly pronounced in Malaysia, where one study reported an alarmingly high prevalence of stress among undergraduate students, with 62.3% experiencing significant stress levels [8]. A major challenge in addressing this widespread problem is the lack of adequate resources to meet the growing demand for support within educational institutions [9]. Furthermore, the shortage of mental health professionals and specialised services in Malaysia exacerbates the issue by creating financial barriers that hinder students from seeking professional help [10].\u003c/p\u003e\n\u003cp\u003eProlonged exposure to unmitigated stress can lead to severe psychological consequences. According to the Conservation of Resources (COR) Theory, stress is a process involving the actual or threatened loss of valuable resources, leading to a state of depletion [11]. Individuals experiencing high stress levels often suffer from diminished cognitive and emotional resources, which can initiate loss spirals that undermine well-being [12]. This depletion is harmful to the psychological resources required for resilience [11] and significantly increases the risk of suicidal behaviour [13, 14]. Research highlights that suicide is a leading cause of death among undergraduate students [15]. Meta-analyses have identified a 14% prevalence of suicide-related outcomes within this demographic [16], while other studies report a 15.2% prevalence rate for suicidal ideation [17]. In Malaysia, this trend is particularly alarming, with high-risk suicidal behaviours reported between 20% and 23.8% [13, 14].\u003c/p\u003e\n\u003cp\u003eGiven these challenges and the limited accessibility of psychological resources in Malaysia, it is critical to explore alternative protective factors that can buffer against stress. One such factor is religiosity, which has been shown to play a significant role in building resilience [18]. Religious teachings often provide a sense of meaning, purpose, social support, and coping mechanisms such as optimism and hope\u0026mdash;qualities that align with and reinforce personal resilience [18]. Consistent with this, several Malaysian studies have found positive correlations between religiosity and resilience [19\u0026ndash;21].\u003c/p\u003e\n\u003cp\u003eThe relationship between religiosity and resilience is proposed to be indirect, operating through the mechanism of stress reduction. Resilience Theory offers a clear framework for this pathway. Religiosity can be conceptualised as a multidimensional construct that builds both internal assets (personal faith, meaning, and hope) and external resources (social support within religious communities) [18, 22\u0026ndash;24]. Through these assets and resources, religiosity functions as a stress buffer [25]. This buffering occurs both externally\u0026mdash;through social support [22\u0026ndash;24]\u0026mdash;and internally\u0026mdash;through cognitive and emotional reframing provided by faith, such as Islamic \u003cem\u003eTawakkul\u003c/em\u003e (trust in divine will) [25], the mindful acceptance of suffering in Buddhism [26, 27], forgiveness in Christianity [28, 29], and the karmic understanding of causality in Hinduism [30].\u003c/p\u003e\n\u003cp\u003eThis process is further explained by the Conservation of Resources Theory [11]. The assets and resources derived from religiosity are hypothesised to reduce perceived stress, thereby conserving individuals\u0026rsquo; psychological capacity and preventing further depletion [12]. Reduced stress allows individuals to retain the fuel necessary to rebound and exhibit resilience [3, 31, 32]. Conversely, high perceived stress depletes limited cognitive and emotional resources, creating loss spirals that make individuals more vulnerable to future stressors [12]. This mechanism clarifies why resilience is resource-intensive, requiring focus, optimism, and energy to bounce back. When religiosity buffers perceived stress, these psychological reserves are conserved, providing the foundation for resilience to manifest. Empirical evidence consistently supports this inverse relationship, showing that lower perceived stress significantly predicts higher resilience [5, 16, 33].\u003c/p\u003e\n\u003cp\u003eGiven that the literature supports the link between religiosity and reduced stress, as well as the association between lower stress and greater resilience, this study proposes a mediation model to explain the overall relationship between religiosity and resilience. Specifically, it is hypothesised that perceived stress mediates the relationship between religiosity and resilience, such that higher levels of religiosity lead to reduced perceived stress, which in turn enhances resilience among Malaysian undergraduate students.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 Research Objectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBuilding upon the gaps identified in the literature and the researchers\u0026rsquo; hypotheses, this study aims to investigate the following objectives among undergraduate students in Klang Valley:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eRO1:\u003c/strong\u003e To examine the relationships between religiosity, perceived stress, and resilience.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRO2:\u003c/strong\u003e To explore the mediating effect of perceived stress on the relationship between religiosity and resilience after controlling for gender, religious affiliation and age.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"2. Methods","content":"\u003cp\u003eAll methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1 Research Design and Setting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cross-sectional correlational explanatory study was conducted between September to November 2024 among university students in the Klang Valley, Malaysia. The Klang Valley hosts the largest cluster of reputable public and private universities and colleges, establishing it as the major hub for tertiary education in the country.\u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were recruited based on the following inclusion criteria: (1) Malaysian citizen currently enrolled in an undergraduate programme in the Klang Valley, and (2) individuals who self-identified as adhering to a religion. Exclusion criteria included individuals who identified as atheists, those unable to comprehend the survey instructions, and those currently undergoing medical treatment for stress-related disorders. G*Power software version 3.1 was used to determine the recommended sample size, \u0026nbsp;using an a priori analysis, the F test family for linear multiple regression (fixed model, R\u003csup\u003e2\u003c/sup\u003e deviation from zero). Based on a small-to-medium effect size (\u003cem\u003ef\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e=0.09), derived from the smallest significant predictor, \u003cem\u003er\u003c/em\u003e=-.29 [34], an alpha level of .05, and a power of .80 for a model with two predictors, the required sample size was 89. Accounting for an anticipated attrition rate of 20%, a total of 107 participants were targeted in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Study Procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe convenience sampling strategy was used to recruit participants based on their accessibility [35]. Data were collected online using a Google Form that included the informed consent statement and the survey instruments. The link and QR code to the Google Form were disseminated via social media platforms such as Instagram, Facebook, and email. Upon providing consent, participants were directed to complete the survey questionnaires, which required approximately 10–15 minutes. This recruitment approach was cost-effective. All data were collected anonymously, and confidentiality was ensured throughout the process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Instruments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBackground Information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants’ basic demographic information, including age, gender, and religious affiliation, was collected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerceived Stress Scale (PSS-10)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePerceived stress was assessed using the 10-item Perceived Stress Scale (PSS-10) [36]. Participants rated each item on a 5-point Likert scale (0 = Never to 4 = Very Often) based on their feelings and thoughts over the past month. After reverse-scoring four items, all item scores were summed. Total scores range from 0 to 40, with higher scores indicating greater perceived stress. The PSS-10 has demonstrated good internal consistency (α=.82), test–retest reliability (α=.79) [38, 39], and construct validity [40]. In the present study, the scale demonstrated good internal consistency reliability (α=.85).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSanta Clara Strength of Religious Faith Questionnaire (SCSRFQ)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReligiosity was measured using the 10-item SCSRFQ [37]. This scale evaluates the strength of an individual's religious faith (e.g., \"My religious faith is extremely important to me.\") using a 4-point Likert scale (1 = strongly disagree to 4 = strongly agree). Items scores were summed, with higher scores representing stronger religious faith. The SCSRFQ has shown high internal consistency reliability (\u003cem\u003eα\u003c/em\u003e=.95) [38] and good validity [37]. In the present study, the scale showed excellent internal consistency reliability (\u003cem\u003eα\u003c/em\u003e=.94).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBrief Resilience Scale (BRS)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResilience was measured using the 6-item Brief Resilience Scale (BRS) [39] which assesses an individual’s ability to bounce back from stress (e.g., \"I tend to bounce back quickly after hard times.\"). Participants responded on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). After the reverse-scoring three items, the final score was calculated as the mean of all six items. Higher mean scores indicate greater resilience. The BRS has demonstrated good reliability (\u003cem\u003eα\u003c/em\u003e=.71) [40] and validity in Southeast Asian contexts [41]. In the present study, the scale reported acceptable internal consistency reliability (\u003cem\u003eα\u003c/em\u003e=.73).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Data Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were conducted using IBM SPSS Statistics (Version 29.0). The sample’s background characteristics were analysed using descriptive statistics. The Pearson correlation coefficient was used to determine the strength and direction of the linear relationship between religiosity, perceived stress and resilience. To quantify the mediation effect, the PROCESS Macro (Model 4) developed by Hayes was employed [42]. In this model, religiosity was entered as the predictor, resilience as the outcome, and perceived stress as the mediator. A bootstrapping procedure with 5,000 samples and a 95% confidence interval was conducted to examine the indirect effects. The mediation (indirect) effect was considered statistically significant if the 95% bias-corrected confidence interval (CI) did not include zero.\u003c/p\u003e"},{"header":"3. Results and Discussion","content":"\u003cp\u003e\u003cstrong\u003e3.1 Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 119 participants (Mean=20.90 years, SD=1.31) took part in this study. Most participants were female (51.3%) and affiliated with Buddhism (45.4%). Independent samples t-tests showed no significant gender differences in religiosity, \u003cem\u003et\u003c/em\u003e(117)=0.434, \u003cem\u003ep\u003c/em\u003e=.665, or perceived stress, \u003cem\u003et\u003c/em\u003e(117)=1.159, \u003cem\u003ep\u003c/em\u003e=.249. Similarly, the Welch\u0026rsquo;s t-test indicated no significant gender difference in resilience, \u003cem\u003et\u003c/em\u003e(107.31)=0.022, \u003cem\u003ep\u003c/em\u003e=.983 (Table 1).\u003c/p\u003e\n\u003cp\u003eA one-way ANOVA examining differences across religious affiliation found a significant effect on religiosity, \u003cem\u003eF\u003c/em\u003e(3, 115)=15.423, \u003cem\u003ep\u003c/em\u003e\u0026lt;.001, but no significant differences for perceived stress, \u003cem\u003eF\u003c/em\u003e(3, 115)=0.193, \u003cem\u003ep\u003c/em\u003e=.901, or resilience, \u003cem\u003eF\u003c/em\u003e(3, 115)=0.498, \u003cem\u003ep\u003c/em\u003e=.684. Post hoc comparisons using the Bonferroni correction revealed that Muslim participants reported significantly higher religiosity compared with Buddhist (\u003cem\u003ep\u003c/em\u003e\u0026lt;.001), Christian (\u003cem\u003ep\u003c/em\u003e=.039), and Hindu (\u003cem\u003ep\u003c/em\u003e=.020) participants (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"609\" height=\"644\"\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 presents the results of the Pearson correlation analyses. Age was positively associated with perceived stress (\u003cem\u003er\u003c/em\u003e=.250, \u003cem\u003ep\u003c/em\u003e\u0026lt;.01), but was inversely associated with resilience (\u003cem\u003er\u003c/em\u003e=\u0026ndash;.247, \u003cem\u003ep\u003c/em\u003e\u0026lt;.01). Religiosity was negatively associated with perceived stress (r=\u0026ndash;.290, p\u0026lt;.01) and positively associated with resilience (\u003cem\u003er\u003c/em\u003e=.221, \u003cem\u003ep\u003c/em\u003e\u0026lt;.05). Higher perceived stress was strongly associated with lower resilience (\u003cem\u003er\u003c/em\u003e=\u0026ndash;.762, p\u0026lt;.01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003e\u003cem\u003ePearson correlation coefficients among study variables (N=119)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003col\u003e\n \u003cli\u003eAge\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20.90 \u0026plusmn; 1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003col start=\"2\"\u003e\n \u003cli\u003eReligiosity\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.55 \u0026plusmn; 7.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e- .038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003col start=\"3\"\u003e\n \u003cli\u003ePerceived Stress\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.66 \u0026plusmn; 6.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.250**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-.290**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003col start=\"4\"\u003e\n \u003cli\u003eResilience\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.16 \u0026plusmn; 0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-.247**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.221*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-.762**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: **\u003cem\u003ep\u003c/em\u003e\u0026lt;.01, *\u003cem\u003ep\u003c/em\u003e\u0026lt;.05\u003c/p\u003e\n\u003cp\u003eThe mediation effect of perceived stress on the relationship between religiosity and resilience, controlling for gender, religious affiliation and age, was analysed using the SPSS PROCESS macro (Model 4) by Hayes [42]. As shown in Table 3, religiosity significantly predicted perceived stress (\u003cem\u003e\u0026beta;\u003c/em\u003e=\u0026ndash;0.336, \u003cem\u003et\u003c/em\u003e=-4.016, \u003cem\u003ep\u003c/em\u003e\u0026lt;.001), and perceived stress significantly predicted resilience (\u003cem\u003e\u0026beta;\u003c/em\u003e=\u0026ndash;0.077, \u003cem\u003et\u003c/em\u003e=-11.167, \u003cem\u003ep\u003c/em\u003e\u0026lt;.001). However, the direct effect of religiosity on resilience was not significant after controlling for perceived stress (\u003cem\u003e\u0026beta;\u003c/em\u003e=0.001, \u003cem\u003et\u003c/em\u003e=0.093, \u003cem\u003ep\u003c/em\u003e=.926).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe bootstrapped indirect effect was 0.026, with a 95% CI [0.011, 0.040]. Since the confidence interval did not include zero, the mediation effect was significant. These findings indicate that perceived stress fully mediates the relationship between religiosity and resilience, accounting for 97.72% of the total effects (Table 4). Figure 1 presents the mediation pathway model. Figure 1 illustrates the mediation pathway model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003e\u003cem\u003eRegression model of mediation analysis after controlling for covariates (N=119)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRegression Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eGoodness-of-Fit Indices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eCoefficient significance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOutcome variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePredictor variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePath a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePerceived stress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReligiosity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-4.016*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePath c\u0026rsquo; (direct)\u003c/p\u003e\n \u003cp\u003ePath b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResilience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReligiosity\u003c/p\u003e\n \u003cp\u003ePerceived stress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003cp\u003e-.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003cp\u003e-1.167*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal effect c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResilience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReligiosity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.990*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: *\u003cem\u003ep\u003c/em\u003e\u0026lt;.001\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e \u003cem\u003eBootstrap analysis of multiple mediation effects\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEffect size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;SE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePercentage of Total Effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLower Limit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUpper Limit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal effect (c)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDirect effect (c\u0026rsquo;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.28%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndirect effect (a*b)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e97.72%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Discussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo the best of the authors\u0026rsquo; knowledge, this study is the first to integrate Resilience Theory and Conservation of Resources (COR) Theory to examine the relationships between religiosity, perceived stress, and resilience among undergraduate students. The findings showed that religiosity was associated with lower perceived stress and higher resilience, while perceived stress was strongly and inversely related to resilience. Mediation analysis further demonstrated that perceived stress fully mediated the relationship between religiosity and resilience, whereby religiosity negatively predicted perceived stress, and perceived stress, in turn, negatively predicted resilience.\u003c/p\u003e\n\u003cp\u003eThe present study\u0026rsquo;s first significant pathway (Religiosity \u0026rarr; Perceived Stress, path a) provides robust empirical support for Resilience Theory. Consistent with prior research [25, 29], findings indicate that religiosity acts as a vital buffer against stress. This aligns with Resilience Theory, which conceptualises religiosity as offering both internal assets (personal faith, meaning-making) and external resources (social support) [18, 22\u0026ndash;24]. Externally, engagement in religious activities\u0026mdash;such as prayer, festivals, or scriptural reading\u0026mdash;fosters community interaction, acting as a stress-buffering social resource. Internally, religious doctrines, such as Buddhist mindfulness, equip individuals with cognitive assets to restructure their understanding of setbacks [30].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy specifically measuring on personal faith, this study enriches the \u0026ldquo;faith vs. social support\u0026rdquo; debate [30], suggesting that beyond social benefits, religion functions as a powerful cognitive mechanism for reappraising stress. Furthermore, within the framework of COR Theory, religiosity functions as a critical mechanism for maintaining cognitive and emotional capital, thereby helping individuals conserve or replenish their resource pool amidst stressful circumstances [12]. Consequently, individuals with higher religiosity perceive less stress, as they are better equipped with effective, religiously informed coping strategies to manage challenging circumstances.\u003c/p\u003e\n\u003cp\u003eThe second significant pathway (Perceived Stress \u0026rarr; Resilience, path b) provides empirical support for COR Theory [11]. The theory posits that stress functions as a mechanism of resource depletion; high stress triggers a resource drain that exhausts the finite cognitive and emotional reserves necessary for adaptive coping. This finding confirms a strong inverse relationship between stress and resilience, aligning with prior studies [3, 33, 39, 43]. More specifically, elevated stress directly undermines an individual\u0026apos;s capacity to mount a resilient response by depleting essential assets [12]. Conversely, when stress is reduced, this drain is curtailed, preserving the psychological \u0026ldquo;fuel\u0026rdquo; required to \u0026ldquo;bounce back\u0026rdquo; from adversity. Consequently, individuals retain greater mental energy to cultivate core resilient attributes\u0026mdash;such as self-control, optimism, and mindfulness[12]. By conserving these cognitive resources, individuals are better positioned to build and maintain the fundamental characteristics of a resilient individual.\u003c/p\u003e\n\u003cp\u003eTogether, these findings explain the full mediation mechanism of perceived stress in the relationship between religiosity and resilience, which is aligned with past research [20, 21, 24]. A key insight is the theoretical parsimony of the model: religiosity, a relatively stable internal and external resource, is logically positioned to buffer perceived stress\u0026mdash;a more transient psychological state [44, 45]. This supports an integrated interpretation of Resilience Theory, which views religiosity as both an internal asset and an external resource [18, 22\u0026ndash;24], alongside COR Theory\u0026rsquo;s emphasis on the depletion and conservation of coping resources [11]. The findings suggest that religiosity contributes resources that function as stress buffers, conserving the psychological capacity needed to bounce back and enhance resilience [3, 33, 39, 43]. This also clarifies the non-significant direct effect of religiosity on resilience (path c\u0026rsquo;), indicating that the previously observed \u0026ldquo;total effect\u0026rdquo; primarily reflected an unobserved indirect pathway through stress.\u003c/p\u003e\n\u003cp\u003eThe primary insight of this mediation model is that religiosity does not enhance resilience directly. Instead, its influence is fully channelled through its role in reducing perceived stress\u0026mdash;a more fundamental mechanism. This highlights perceived stress as the \u0026ldquo;active ingredient\u0026rdquo; linking religiosity to resilience. This has important implications for intervention design. It suggests that resilience is strengthened not by religiosity per se but through the stress-coping processes embedded within religious teachings and practices. Practically, individuals benefit from engaging in religious activities that reduce stress, rather than relying on the abstract belief that religiosity inherently builds resilience. An alternative explanation for the non-significant direct effect is that religious identity alone (as measured in this study) may be insufficient; active practice of one\u0026rsquo;s faith may be required to activate the stress-buffering mechanism. This remains an important avenue for future research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Public Health Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis mediation model offers significant public health implications, particularly for university populations facing a well-documented lack of accessible mental health resources [9, 10]. First, the model identifies stress reduction as the primary mechanism for building resilience. This highlights a powerful, preventive public health strategy, suggesting that institutions can foster resilience not only by treating students but also by implementing systemic stress-reduction policies. For universities, this implies that policies addressing student workload, examination schedules, and financial stressors are not merely administrative decisions but represent tangible mental health interventions. Moreover, this study provides empirical\u0026nbsp;support for culturally embedded, non-medicalised mental health pathways. In a multicultural nation such as Malaysia, the findings suggest that strengthening existing and trusted community structures (e.g., Buddhist societies, Muslim student associations) can serve as a highly effective, low-cost, and feasible public health approach to supporting student well-being. These results may also offer religious student organisations insight into developing stress-reduction-focused programmes that indirectly enhance resilience. Finally, by demonstrating that faith-based practices play a significant role in the stress\u0026ndash;resilience pathway, this research helps destigmatise help-seeking outside formal clinical settings. This is crucial, as it empowers students to utilise resources that are already accessible, culturally meaningful, and personally relevant to them.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Limitations and Future Research\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe generalisability of the findings is limited by the use of convenience sampling and the geographical restriction to the Klang Valley. These constraints reduce the ability to capture the Malaysia\u0026rsquo;s broader multiethnic and multi-faith undergraduate population. Future studies should adopt multistage or multicentre sampling to obtain a more representative range of religious affiliations. Additionally, the cross-sectional correlational design prevents any causal inferences. Longitudinal or experimental designs are recommended to more accurately establish the temporal and causal relationships among religiosity, perceived stress, and resilience.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Conclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study successfully clarified the complex relationship between religiosity, perceived stress, and resilience. It moves beyond simple correlations to demonstrate a clear mechanism: religiosity does not directly build resilience but enhances it by first reducing individual\u0026rsquo;s perceived stress. These insights are crucial for developing more effective, culturally sensitive, and resource-efficient mental health interventions for university students.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAll methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received approval from the Ethics Committee of Tunku Abdul Rahman University of Management and Technology (TAR UMT). Informed consent was obtained digitally from all participants prior to their participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAI Usage Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLanguage editing assistance was provided using an AI-based writing tool to improve clarity and academic style. All content and interpretations remain the authors\u0026rsquo; original work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no financial support for the research, authorship, or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to participant privacy and ethical restrictions but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVanMeter F, Cicchetti D. 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The role of religion in buffering the impact of stressful life events on depressive symptoms in patients with depressive episodes or adjustment disorder. Int J Environ Res Public Health. 2019;16:1238. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph16071238\u003c/span\u003e\u003cspan address=\"10.3390/ijerph16071238\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"perceived stress, religiosity, resilience, mediation, undergraduate students","lastPublishedDoi":"10.21203/rs.3.rs-8873152/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8873152/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction/Objectives\u003c/h2\u003e \u003cp\u003eReligiosity is widely recognised as a protective factor in psychological well-being, yet the mechanisms linking religiosity to resilience remain insufficiently understood. Grounded in Resilience Theory and Conservation of Resources Theory, this study examined the mediating role of perceived stress in the relationship between religiosity and resilience among undergraduate students.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional explanatory correlational study involved 119 undergraduates (51.3% female; Mean\u003csub\u003eage\u003c/sub\u003e = 20.90, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.31;) who completed the Santa Clara Strength of Religious Faith Questionnaire (SCSRFQ), Perceived Stress Scale (PSS-10), and Brief Resilience Scale (BRS) online via Google Forms. Mediation analysis was conducted using the PROCESS macro (Model 4) with 5,000 bootstrap samples and a 95% confidence interval.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAnalysis showed that perceived stress fully mediated the relationship between religiosity and resilience (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026, 95% \u003cem\u003eCI\u003c/em\u003e[0.011, 0.040]), accounting for 97.72% of the total effect. Religiosity significantly predicted lower perceived stress (\u003cem\u003eβ\u003c/em\u003e=\u0026ndash;0.336, \u003cem\u003ep\u003c/em\u003e\u0026lt;.001), and in turn, perceived stress significantly predicted lower resilience (\u003cem\u003eβ\u003c/em\u003e=\u0026ndash;0.077, \u003cem\u003ep\u003c/em\u003e\u0026lt;.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese findings suggest that religiosity enhances resilience not directly, but through its stress-buffering function. This study offers a theoretically integrated model and highlights stress reduction as a key mechanism for strengthening resilience among undergraduate students.\u003c/p\u003e","manuscriptTitle":"Perceived stress as a mediator between religiosity and resilience among Malaysian undergraduates","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-13 09:31:19","doi":"10.21203/rs.3.rs-8873152/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-28T07:25:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-24T02:41:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-23T01:42:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121921894015116447121948992366799443181","date":"2026-03-13T02:56:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309521204060768913334608976409728158749","date":"2026-03-13T01:37:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-10T03:02:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-09T12:03:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-08T09:11:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2026-03-08T09:07:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3b840886-b0e6-4dea-a1fc-370169d1edac","owner":[],"postedDate":"March 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-19T05:25:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-13 09:31:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8873152","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8873152","identity":"rs-8873152","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}