Strengths-Based Cognitive Behavioral Therapy: A Longitudinal Randomized Controlled Trial in Students

Abstract

Purpose

This study examined the effectiveness of Strengths-Based Cognitive Behavioral Therapy (SBCBT) in reducing psychological distress and enhancing resilience among university students.

Methods

A longitudinal randomized controlled trial was conducted with 60 students experiencing mild-to-moderate depression, anxiety, and stress and low resilience. Participants were randomly assigned to either an 8-week SBCBT intervention or a Cognitive Behavioral Therapy-based self-help control condition. Outcomes were assessed at baseline, postintervention, and one-month follow-up using the depression, anxiety, and stress and Brief Resilience Scale.

Results

Generalized Estimating Equations revealed significant Time × Group interactions across all outcomes. In the SBCBT group, depression decreased from 14.62 to 9.19, anxiety from 11.08 to 8.01, and stress from 20.45 to 13.45. Resilience increased from 2.41 to 3.48. Between-group analyses indicated large effect sizes, particularly for stress reduction (d = −1.08) and resilience improvement (d = 1.65).

Discussion

Findings support SBCBT as an effective, resilience-focused intervention for improving student mental health and well-being in university settings.

Keywords

Cognitive behavioral therapy psychological resilience depression anxiety stress

Mental health challenges have emerged as a critical public health issue worldwide, with increasing prevalence across diverse populations (Wainberg et al., 2017). University students represent one of the most vulnerable groups, as they often face multiple, intersecting stressors that negatively influence their well-being (Barbayannis et al., 2022; Gao et al., 2025). These include demanding academic workloads, financial strain, difficulties in building and maintaining social networks, and the challenges of adapting to new living environments (Beiter et al., 2015; Kirkbride et al., 2024; Stallman, 2010). Research has consistently shown that university students report significantly higher levels of depression, anxiety, and stress (DASS-21) compared to their nonstudent peers (Eisenberg et al., 2007; Larcombe et al., 2016; Mofatteh, 2021). The onset of these mental health problems during early adulthood is particularly concerning, as it can disrupt developmental milestones such as identity formation, career planning, and the establishment of long-term relationships (Schlack et al., 2021; Duffy et al., 2020). Furthermore, untreated psychological distress in students has been linked to academic underperformance, dropout, impaired social functioning, and long-term adverse health outcomes (Hunt & Eisenberg, 2010). Thus, the mental health of university students is not only a matter of individual well-being (Gao et al., 2025) but also a broader societal concern with implications for educational systems, healthcare services, and workforce development (Hyseni Duraku, Davis & Hamiti, 2023; Li et al., 2025).

Within this context, resilience has gained prominence as a central construct in understanding adaptive responses to adversity (Infurna et al., 2024). Resilience is generally defined as the capacity to withstand, adapt to, and recover from significant stress or trauma, while maintaining or regaining psychological well-being (Masten, 2014). Importantly, resilience is now widely conceptualized as a dynamic process that develops through the interaction of individual, relational, and contextual factors, rather than as an immutable personal trait (Ungar, 2011). The ecological model of resilience emphasizes that protective processes operate across multiple domains, including cognitive (e.g., problem-solving, flexible thinking), emotional (e.g., regulation of negative affect, positive appraisal), behavioral (e.g., persistence, goal-directed actions), interpersonal (e.g., supportive relationships, social competence), and physical (e.g., adequate sleep, physical activity) (Troy et al., 2023; Fergus & Zimmerman, 2005). These multidimensional resources act as buffers that can reduce the negative impact of challenging life situations, such as poverty, discrimination, chronic illness, or social exclusion. By doing so, they promote positive developmental outcomes, even in the face of significant challenges. During adolescence and emerging adulthood periods marked by exploration, identity formation, and increased sensitivity to stress, the development of resilience is especially vital (Xing, Xu, Li & Luo, 2023; Taylor et al., 2017). Interventions that enhance resilience have been shown to reduce psychological distress, enhance academic performance, and strengthen social-emotional competencies (Durlak et al., 2011; Hartley, 2011; Pan & Zhuang, 2024). Resilience was selected as the central intervention target because it is a key protective factor against psychological distress in university students. Given the high levels of academic stress and emotional difficulties in this population, strengthening resilience is expected to enhance adaptive coping, reduce symptoms of DASS-21, and support longer-term psychological well-being, making it a theoretically and practically justified focus of intervention.

Cognitive Behavioral Therapy (CBT) is one of the most extensively researched and empirically validated interventions for mental health problems, with demonstrated efficacy in reducing DASS-21 across diverse populations (Hofmann et al., 2012). The foundational principle of CBT is that maladaptive thoughts and behaviors contribute to emotional distress, and that modifying these cognitions and behaviors can promote more adaptive functioning. Traditional CBT interventions focus on identifying and restructuring dysfunctional beliefs, practicing adaptive coping skills, and engaging in behavioral activation to improve emotional well-being. Contemporary developments in psychotherapy highlight the limitations of a solely deficit-based approach and emphasize the importance of integrating recovery-oriented and strengths-based perspectives (Rashid, 2015; Slade, 2010). Recovery-oriented approaches prioritize personal growth, meaning, and empowerment, shifting the therapeutic focus from symptom reduction to the cultivation of personal strengths and long-term well-being.

Although CBT is well-established in reducing psychological distress across populations (Hofmann et al., 2012), recent developments in psychotherapy have increasingly emphasized the integration of strengths-based and resilience-oriented approaches to enhance treatment outcomes. Strengths-based CBT (SBCBT) extends traditional CBT by shifting the therapeutic focus from symptom reduction toward the identification and systematic utilization of personal strengths as central mechanisms of change (Padesky & Mooney, 2012). This strengths-oriented perspective aligns with recovery-focused and positive psychology approaches that emphasize psychological growth, meaning, and empowerment alongside symptom alleviation (Rashid, 2015; Slade, 2010). Strengths-based and recovery-oriented interventions have been shown to improve psychological functioning by enhancing individuals’ perceived resources, self-efficacy, and adaptive coping capacities rather than focusing solely on psychopathology (Slade, 2010; Yuen et al., 2020). Empirical studies directly examining fully integrated SBCBT remain limited, particularly among university students. Most existing research has examined CBT or strengths-based approaches separately rather than in a unified framework, highlighting the need for further randomized controlled trials testing the combined effect of SBCBT on both psychological distress and resilience in the student population.

Strengths-based CBT represents one such adaptation, blending the core methods of CBT with strategies explicitly designed to build resilience and psychological strengths. The SBCBT encourages individuals to recognize and enhance their self-efficacy, cultivate positive emotions, foster a sense of meaning and purpose in life, and leverage personal and social resources to navigate adversity (Yuen et al., 2020; Padesky & Mooney, 2012). Empirical studies suggest that integrating strengths-based or spiritual elements into CBT can yield additional benefits. Spiritually adapted CBT has been shown to improve hope, peace, and spiritual health in individuals recovering from opioid use disorder (Sonbol et al., 2025). Acceptance-and strengths-focused interventions such as the “EmpowerU” program have demonstrated success in enhancing resilience, promoting self-discovery, and improving overall well-being among adolescents and university students (Arslan, Aydoğdu & Uzun, 2025). These findings align with the behavioral activation model, which emphasizes that consistent engagement in meaningful, goal-directed activities can alleviate depressive symptoms and reinforce adaptive patterns of behavior (Martell et al., 2010). Collectively, such approaches highlight the therapeutic value of interventions that go beyond correcting deficits to actively build enduring psychological resources (Wang & Feng, 2022).

Despite these encouraging findings, the evidence for SBCBT is still limited, especially among university students. Although resilience training, CBT, and strengths-based approaches have each been shown to be effective on their own, only a small number of experimental studies have tested the combined use of strengths-based strategies within a CBT framework to improve resilience and mental health in student populations (Xiang, Wan & Zhu, 2025). This gap is important to address, as university students are a vulnerable group who face a range of academic, developmental, and psychosocial stressors. Improving resilience in this population is important not only for mental health but also for academic functioning and longer-term well-being. This intervention was guided by a clear logic model linking student needs, intervention mechanisms, and expected outcomes. University students often experience elevated psychological distress due to academic demands and limited coping resources, which are typically maintained by maladaptive cognitions and reduced self-efficacy. Accordingly, SBCBT was selected because it integrates cognitive restructuring with strengths identification and resilience-building strategies. The intervention components (e.g., strengths identification and application, and Personal Model of Resilience [PMR] development) are designed to enhance adaptive thinking and coping, leading to reduced DASS-21, and improved resilience. Therefore, this study aims to examine the effectiveness of a SBCBT intervention among university students. The main aim is to assess whether SBCBT reduces symptoms of DASS-21, and the secondary aim is to examine whether it improves resilience (Brief Resilience Scale, BRS). By combining standard CBT techniques with strengths-based strategies, this study seeks to provide evidence for a more integrated approach to intervention and contribute to research on resilience and mental health in university students.

Method

Research Design

This study employed a repeated-measures, randomized controlled experimental design with two parallel groups (intervention and control). Assessments were conducted at three time points: baseline (pretest), immediately after the intervention (posttest), and at follow-up (4 weeks after posttest).

Participants and Sampling

The target population of this study comprised undergraduate university students. Recruitment was conducted through the distribution of brochures across various campus platforms, utilizing convenience sampling. Students were eligible to participate if they (a) provided informed consent, (b) scored within the mild-to-moderate range on the DASS-21 (Lovibond & Lovibond, 1995), and (c) obtained a low score on the BRS (Smith et al., 2008). Sample size estimation was conducted using G*Power version 3.1.9.7 (Faul et al., 2007). With parameters set at a medium effect size (f = 0.25), an alpha level of .05, and a power of .95, the required sample size was determined to be 60. Accordingly, 60 eligible students were recruited and randomly allocated into two equal groups of 30 participants each (intervention and control) using computer-generated random numbers. To ensure allocation concealment, the computer-generated random allocation sequence was held by an independent researcher who was not involved in the recruitment or assessment processes. The primary investigator was only informed of a participant's group assignment after the participant had formally provided informed consent and completed all baseline assessments.

Procedure

Participants were recruited between June and October 2025 following ethical approval through recruitment brochures disseminated on student platforms. Following approval, recruitment brochures were disseminated on various student platforms. After screening the responses, 70 participants who met the inclusion criteria were contacted and enrolled, with 35 participants randomly allocated to each group. During the intervention and follow-up phases, 10 participants discontinued their participation due to scheduling difficulties, including competing personal and family commitments, time constraints, and travel-related challenges, as well as nonresponsiveness to repeated scheduling attempts) (see Figure 1). Consequently, the final analytic sample comprised 60 participants, with 30 participants in each group.

Figure 1.

CONSORT diagram for enrollment of the participants.

Assessments were conducted at baseline (pretest), immediately after the 8-week intervention (posttest), and 4 weeks later (follow-up). The intervention followed Padesky and Mooney's (2012) four-step SBCBT model. The process began with searching for strengths, where participants were guided to identify and acknowledge their personal assets and positive qualities. Building on this foundation, they were encouraged to construct a PMR by developing an individualized framework that highlighted how these strengths could be organized and mobilized to foster resilience. Once the PMR was established, participants learned to apply it to areas of difficulty, using their resilience strategies to address and manage real-life challenges. Finally, participants engaged in practicing resilience, which involved reinforcing and consolidating these skills through repeated application across various contexts, thereby promoting long-term adaptability and psychological well-being.

Intervention Group

Participants in the intervention group engaged in an 8-week SBCBT program grounded in the model proposed by Padesky and Mooney (2012). The sessions were conducted face-to-face in small group settings, once per week, with each session lasting approximately 60 min. Each intervention activity was selected based on cognitive-behavioral and strengths-based theoretical principles to ensure alignment between therapeutic mechanisms (e.g., cognitive restructuring, self-efficacy enhancement, and adaptive coping) and targeted outcomes of reduced psychological distress and improved resilience. The SBCBT framework is specifically designed to enhance resilience through a structured four-step process. The first step, searching for strengths, involved guiding participants to identify and acknowledge their personal strengths and positive qualities.

The second step involved the development of a PMR, in which participants integrated identified strengths into coherent adaptive strategies. The third step focused on applying the PMR to personally relevant difficulties, enabling participants to translate strengths into adaptive responses to real-life challenges. The final step emphasized repeated practice of resilience strategies across contexts to facilitate skill consolidation and long-term adaptation. Collectively, this process reflects a shift from a deficit-oriented approach toward strength development and psychological growth.

The SBCBT was delivered in a group format due to its alignment with social learning and cognitive-behavioral principles. Group-based delivery facilitates peer learning, normalization of shared experiences, and social reinforcement of adaptive cognitions and behaviors. It also enables participants to benefit from both therapist-guided strengths identification and peer modeling, which may enhance the internalization of resilience-related skills. Previous research supports the effectiveness of group CBT in reducing psychological distress and improving accessibility and efficiency in university settings (Hofmann et al., 2012). Accordingly, the group format was considered both theoretically consistent and practically suitable for the present student sample. Table 1 presents the weekly intervention structure, including session objectives, core components, mechanisms of change, and expected outcomes.

Table 1.

Strengths-Based Cognitive Behavioral Therapy (SBCBT) Intervention Protocol and Session-Level Logic Model.

Session	Focus	Activities	Mechanism of change	Expected outcomes
1	Orientation and group formation	Program introduction, goal setting, establishment of group norms	Group cohesion and therapeutic alliance formation	Increased engagement, rapport, and perceived safety
2	Self-concept and strengths identification	Identification of 5–10 personal strengths; guided reflection	Strength's awareness and positive reappraisal	Enhanced self-awareness and positive self-schema
3	Application of strengths	Use of strengths in guided scenarios and cognitive exercises	Cognitive restructuring through strengths activation	Improved adaptive thinking and cognitive flexibility
4	Personal Model of Resilience (PMR) development	Reflection on past coping experiences and success narratives	Meaning-making and autobiographical integration	Increased insight into effective coping strategies
5	PMR application to challenges	Role-plays, group problem-solving, scenario-based exercises	Behavioral rehearsal and social learning	Improved problem-solving skills and coping confidence
6	Real-life application	Homework assignments and strengths application in daily life	Skill generalization and behavioral activation	Transfer of skills to real-world stressors
7	Consolidation and reinforcement	Group reflection, feedback, and review of progress	Reinforcement and metacognitive awareness	Strengthened resilience integration and self-efficacy
8	Termination and evaluation	Summary of intervention, feedback, postassessment (DASS-21, BRS)	Consolidation and closure	Stabilization of gains and outcome evaluation
Follow-up (week 14)	Maintenance assessment	Follow-up evaluation using DASS-21 and BRS	Maintenance of change and relapse monitoring	Assessment of sustained intervention effects

Control Group

Participants in the control group did not receive structured therapy. Instead, they were provided with written self-help materials derived from CBT principles, including worksheets and activities designed to promote reflective practice and basic coping strategies.

Treatment Fidelity

Treatment fidelity and intervention integrity were ensured through a fully manualized SBCBT protocol designed to maximize standardization and replicability. The manual specified detailed session-by-session objectives, structured therapeutic procedures, in-session activities, therapist prompts, and standardized homework assignments, thereby reducing variability in intervention delivery. Implementation fidelity was further strengthened through consistent adherence to the protocol across all sessions, supported by a structured adherence checklist completed by the therapist after each session to monitor compliance with prescribed procedures and session content. In addition, periodic clinical supervision was conducted throughout the intervention to review session delivery, address potential deviations from the protocol, and minimize therapist drift. Although independent fidelity coding or formal interrater reliability assessment was not conducted, the combination of manualization, structured adherence monitoring, and ongoing supervision provided a robust framework for maintaining high levels of treatment integrity, implementation consistency, and methodological rigor across the trial.

Instruments

Depression, Anxiety, and Stress Scale-21 Items

The DASS-21, developed by Peter F. Lovibond and Sydney H. Lovibond at the University of New South Wales in 1995, is a widely used self-report instrument designed to assess emotional distress across three domains: DASS-21. Each domain contains seven items rated on a 4-point scale from 0 (“did not apply at all”) to 3 (“applied very much or most of the time”). To calculate scores, responses for each subscale are summed and then multiplied by two to correspond with the norms of the original 42-item version. Recommended severity cutoffs classify scores into normal, mild, moderate, severe, or extremely severe ranges: for depression (0–9 normal; 28 + extremely severe), anxiety (0–7 normal; 20 + extremely severe), and stress (0–14 normal; 34 + extremely severe). The DASS-21 is based on a dimensional model of psychological distress, distinguishing differences in severity rather than assigning categorical diagnoses. It has demonstrated high reliability and validity across diverse populations, with strong internal consistency for all subscales. While it offers a valuable snapshot of emotional states over the past week and is particularly useful for screening and monitoring changes over time, it does not assess suicidal ideation and should not replace a clinical interview; elevated scores should be interpreted with caution and followed by professional evaluation when necessary. In the current study, the DASS-21 demonstrated excellent internal consistency, with a total Cronbach's alpha of 0.91, indicating high reliability of the scale in the present sample.

Brief Resilience Scale

The BRS, developed by Smith et al. (2008), is a concise tool designed to assess an individual's ability to bounce back or recover from stress. Unlike many other resilience measures that incorporate protective factors, such as coping strategies or social support, the BRS focuses specifically on resilience as a recovery process following adversity. It consists of six self-report items rated on a 5-point Likert scale ranging from 1 = Strongly Disagree to 5 = Strongly Agree. Three items (2, 4, and 6) are reverse-scored before calculating the total score, which ranges from 6 to 30. The mean score is then derived by dividing the total by 6. The interpretation of scores suggests that a mean between 1.00 and 2.99 indicates low resilience, 3.00 to 4.30 reflects normal resilience, and a mean of 4.31 to 5.00 indicates high resilience. Psychometric evaluations demonstrate strong reliability with Cronbach's alpha ranging from 0.80 to 0.91, along with evidence of validity as indicated by positive correlations with optimism, purpose in life, and social support, and negative correlations with anxiety, depression, negative affect, and perceived stress. The BRS has a one-factor structure representing “resilience as recovery” and is advantageous due to its brevity, direct measurement of the capacity to recover from stress, and applicability across clinical, educational, and research contexts.

Data Analysis

Prior to analysis, data were screened for missing values, normality, and outliers; no serious violations were detected. Descriptive statistics were first computed to examine group trajectories across DASS-21, and resilience at three assessment points (pretest, posttest, and follow-up). Generalized Estimating Equation (GEE) analyses were then conducted to evaluate group differences over time. Pairwise comparisons with mean differences (MDs) and 95% confidence intervals were used to identify within-group changes across time points. The GEE was specified with an appropriate working correlation structure, and baseline values were included as covariates where applicable. Effect sizes (Cohen's d) were calculated for both within-group and between-group comparisons. Within-group effect sizes were computed using the mean change divided by the pooled standard deviation of the respective time points. Between-group effect sizes were calculated using the difference between group means divided by the pooled standard deviation. All analyses revealed significant group × time interactions, indicating that the intervention group showed substantial reductions in DASS-21, as well as significant increases in resilience, compared to the control group, which remained largely stable.

Hypothesis 1:

Posttreatment, the SBCBT group will display statistically significantly lower Depression scores, compared to the control group, as assessed by the Depression subscale of the DASS-21.

Hypothesis 2:

Posttreatment, the SBCBT group will display statistically significantly lower Anxiety scores, compared to the control group, as assessed by the Anxiety subscale of the DASS-21.

Hypothesis 3:

Posttreatment, the SBCBT group will display statistically significantly lower Stress scores, compared to the control group, as assessed by the Stress subscale of the DASS-21.

Hypothesis 4:

Posttreatment, the SBCBT group will display statistically significantly higher Resilience scores, compared to the control group, as assessed by the BRS.

Results

Demographics

A total of 60 undergraduate students participated in the study, with 30 allocated to the intervention group and 30 to the control group. Participants were aged between 19 and 23 years (M = 20.78, SD = 0.86). The overall sample included 38 females (63.3%) and 22 males (36.7%). In the intervention group, 20 participants (66.7%) were female and 10 (33.3%) were male, whereas the control group comprised 18 females (60.0%) and 12 males (40.0%). The groups were comparable in terms of gender distribution, indicating successful randomization.

Baseline Equivalence

All participants were enrolled in the Psychology and Counselling program. Independent samples t-tests and chi-square analyses confirmed baseline equivalence across all primary variables. No statistically significant differences were observed between the intervention and control groups in age (p = .658), depression (p = .818), anxiety (p = .779), stress (p = .378), or resilience (p = .486) at baseline (see Table 2). The participants reported moderate psychological distress at baseline, with a combined mean DASS-21 score of approximately 47.33, consistent with the study's inclusion criteria.

Table 2.

Baseline Demographic and Clinical Characteristics of the Study Participants.

Characteristics	Total (N = 60)	Intervention (n = 30)	Control (n = 30)
	M (SD)	M (SD)	M (SD)	t / χ²	p value
Age	20.78 (0.87)	20.83 (.91)	20.73 (.83)	.44	.658
Depression	15.20 (3.33)	15.30 (3.17)	15.10 (3.53)	.23	.818
Anxiety	11.93 (2.73)	12.03 (2.89)	11.83 (2.60)	.28	.779
Stress	20.20 (3.48)	20.60 (3.57)	19.80 (3.41)	.89	.378
Resilience	2.41 (0.39)	2.45 (.36)	2.38 (.42)	.70	.486

Table 3 illustrates distinct trajectories of psychological adjustment in the therapy and control groups across three assessment points.

Table 3.

Means, 95% Confidence Intervals, and Standard Errors for Depression, Anxiety, Stress, and Resilience in Therapy and Control Groups Across Three Time Points.

Variable	Time	Group
		Therapy				Control
		Mean	95.0% lower CL for mean	95.0% upper CL for mean	Standard error of mean	Mean	95.0% lower CL for mean	95.0% upper CL for mean	Standard error of mean
Depression	Pretest	15.30	14.11	16.49	0.58	15.10	13.78	16.42	0.64
	Posttest	9.87	8.14	11.60	0.85	13.80	12.38	15.22	0.69
	Follow up	10.50	8.65	12.35	0.91	14.10	12.62	15.58	0.72
Anxiety	Pretest	12.03	10.95	13.11	0.53	11.83	10.86	12.80	0.47
	Posttest	8.97	7.48	10.45	0.73	11.20	10.03	12.37	0.57
	Follow up	9.20	7.65	10.75	0.76	11.40	10.20	12.60	0.59
Stress	Pretest	20.60	19.27	21.93	0.65	19.80	18.53	21.07	0.62
	Posttest	13.60	11.77	15.43	0.90	18.27	16.90	19.63	0.67
	Follow up	14.60	12.62	16.58	0.97	18.47	17.14	19.79	0.65
Resilience	Pretest	2.45	2.32	2.58	0.07	2.38	2.22	2.54	0.08
	Posttest	3.52	3.26	3.78	0.13	2.55	2.38	2.72	0.08
	Follow up	3.38	3.11	3.65	0.13	2.50	2.32	2.68	0.09

Hypothesis 1: Depression

Hypothesis 1 predicted that, posttreatment, participants in the SBCBT group would report significantly lower depression scores than participants in the control group, as measured by the Depression subscale of the DASS-21. Descriptive findings indicated a marked reduction in depression among participants receiving SBCBT. Mean depression scores decreased from 15.30 (95% CI [14.11, 16.49]) at pretest to 9.87 (95% CI [8.14, 11.60]) at posttest and remained substantially below baseline at follow-up (M = 10.50, 95% CI [8.65, 12.35]). In contrast, participants in the control group demonstrated only minor fluctuations across the three assessment points (pretest M = 15.10, posttest M = 13.80, follow-up M = 14.10; see Figure 2). The GEE analysis further confirmed that depression scores were significantly lower in the SBCBT group than in the control group at both posttest (MD = −3.93, p = .001) and follow-up (MD = −3.60, p = .003). These findings indicate that SBCBT produced meaningful and sustained reductions in depressive symptoms. Therefore, Hypothesis 1 was supported.

Figure 2.

The depression scores for intervention and control groups across baseline, posttest, and one-month follow-up.

Hypothesis 2: Anxiety

Descriptive findings indicated a substantial reduction in anxiety among participants receiving SBCBT. Mean anxiety scores decreased from 12.03 (95% CI [10.95, 13.11]) at pretest to 8.97 (95% CI [7.48, 10.45]) at posttest and remained relatively stable at follow-up (M = 9.20, 95% CI [7.65, 10.75]). In contrast, anxiety scores in the control group showed minimal variation across the three assessment points (M range = 11.20–11.83; see Figure 3). Descriptive results suggest that participants who received SBCBT experienced meaningful reductions in anxiety symptoms, whereas little change was observed in the control group.

Figure 3.

The anxiety scores for intervention and control groups across baseline, posttest, and one-month follow-up.

Hypothesis 3: Stress

Descriptive findings indicated a marked reduction in stress among participants receiving SBCBT. Mean stress scores decreased from 20.60 (95% CI [19.27, 21.93]) at pretest to 13.60 (95% CI [11.77, 15.43]) at posttest, with scores remaining relatively stable at follow-up (M = 14.60, 95% CI [12.62, 16.58]). In contrast, the control group demonstrated only minor fluctuations across the three assessment points (pretest M = 19.80; posttest M = 18.27; follow-up M = 18.47; see Figure 4).

Figure 4.

The stress scores for intervention and control groups across baseline, posttest, and one-month follow-up.

Hypothesis 4: Resilience

In contrast, resilience showed an increasing trend in the intervention group, rising from pretest (M = 2.45, 95% CI [2.32, 2.58]) to posttest (M = 3.52, 95% CI [3.26, 3.78]), with a small decline at follow-up (M = 3.38, 95% CI [3.11, 3.65]) while remaining above baseline levels. The control group demonstrated only negligible changes across assessments (M range = 2.38–2.55) (see Figure 5). These descriptive findings indicate that the intervention was associated with substantial reductions in DASS-21, alongside meaningful improvements in resilience, with effects largely maintained at follow-up. In contrast, the control group exhibited minimal changes across all outcomes.

Figure 5.

The resilience scale scores for intervention and control groups across baseline, posttest, and one-month follow-up.

Generalized Estimating Equations Analysis

The GEE analysis was employed to examine the longitudinal effects of the intervention across the four outcome variables (see Table 4). Baseline comparisons indicated no significant differences between the intervention and control groups across all measures (p > .05), confirming group equivalence prior to the intervention. The intervention group demonstrated significantly lower depression scores compared to the control group at posttest (MD = −3.93, 95% CI [−6.12, −1.74], p = .001) and follow-up (MD = −3.60, 95% CI [−5.92, −1.28], p = .003). Similarly, anxiety scores were reduced at posttest (MD = −2.23, 95% CI [−4.09, −0.38], p = .019) and follow-up (MD = −2.20, 95% CI [−4.12, −0.28], p = .026). Stress levels also showed significant reductions at posttest (MD = −4.67, 95% CI [−6.90, −2.43], p < .001) and follow-up (MD = −3.87, 95% CI [−6.20, −1.53], p = .002). Regarding resilience, the intervention group showed significantly higher scores than the control group at posttest (MD = 0.97, 95% CI [0.67, 1.27], p < .001) and follow-up (MD = 0.88, 95% CI [0.56, 1.20], p < .001). Effect sizes ranged from moderate to large, with d = 1.65 at posttest and d = 1.41 at follow-up, indicating robust improvements in adaptive functioning alongside reductions in psychological distress.

Table 4.

Results of Generalized Estimating Equations (GEE) in Total Professional Depression, Anxiety, Stress, and Resilience Score.

Variable	Time	(I) Group	(J) Group	Mean difference (I-J)	Std. error	df	Sig.	95% Wald confidence interval for difference		Effect size d
Variable	Time	(I) Group	(J) Group	Mean difference (I-J)	Std. error	df	Sig.	Lower	Upper	Effect size d
Depression	Pretest	I	C	0.20	.866	58	.818	−1.53	1.93	0.06
	Posttest	I	C	−3.93a	1.093	58	.001	−6.12	−1.74	−0.93
	Follow up	I	C	−3.60a	1.161	58	.003	−5.92	−1.28	−0.80
Anxiety	Pretest	I	C	0.20	.710	58	.779	−1.22	1.62	0.07
	Posttest	I	C	−2.23a	.926	58	.019	−4.09	−0.38	−0.62
	Follow up	I	C	−2.20a	.961	58	.026	−4.12	−0.28	−0.59
Stress	Pretest	I	C	0.80	.901	58	.378	−1.00	2.60	0.23
	Posttest	I	C	−4.67a	1.117	58	<.001	−6.90	−2.43	−1.08
	Follow up	I	C	−3.87a	1.165	58	.002	−6.20	−1.53	−0.86
Resilience	Pretest	I	C	0.07	.100	58	.486	−0.13	0.27	0.18
	Posttest	I	C	0.97a	.151	58	<.001	0.67	1.27	1.65
	Follow up	I	C	0.88a	.161	58	<.001	0.56	1.20	1.41

Note. Pairwise comparisons of estimated marginal means based on the original scale of the dependent variable depression; I = Intervention; C = Control. a. The mean difference is significant at the .05 level.

The pairwise comparisons revealed clear temporal changes in the intervention group relative to the control condition (see Table 5). A significant reduction in depression was observed in the intervention group from pretest to posttest (M_diff = 5.43, SE = 1.06, p < .001, 95% CI [3.25, 7.62], d = .93), which remained significant at follow-up (M_diff = 4.80, SE = 1.14, p < .001, 95% CI [2.46, 7.14], d = .76). The difference between posttest and follow-up was not statistically significant (M_diff = −0.63, SE = 1.24, p = .616, 95% CI [−3.19, 1.92]), suggesting that treatment gains were maintained over time. In contrast, no significant changes were detected in the control group across any time points (all p > .05). A similar pattern emerged for anxiety. The intervention group demonstrated a significant decrease from pretest to posttest (M_diff = 3.07, SE = .77, p < .001, 95% CI [1.50, 4.64], d = .73), as well as from pretest to follow-up (M_diff = 2.83, SE = .96, p = .006, 95% CI [0.87, 4.79], d = .54), whereas the posttest to follow-up comparison was not significant (M_diff = −0.23, SE = .90, p = .796, 95% CI [−2.06, 1.60]). Meanwhile, anxiety levels in the control group remained stable, with no statistically significant differences observed over time (all p > .05).

Table 5.

Pairwise Comparison of Professional Depression, Anxiety, Stress, and Resilience Scores Across Time.

Variable	Group	(I)Test	(J) Test	Mean difference (I-J)	SE	p value	95.0% lower CL for mean	95.0% upper CL for mean	Effect size d
Depression	I	Pretest	Posttest	5.43a	1.06	<.001	3.25	7.62	.93
		Pretest	Follow up	4.80a	1.14	<.001	2.46	7.14	.76
		Posttest	Follow up	−.63	1.24	.616	−3.19	1.92	−.09
	C	Pretest	Posttest	1.30	.937	.176	−0.62	3.22	.25
		Pretest	Follow up	1.00	1.05	.352	−1.16	3.16	.17
		Posttest	Follow up	−.30	1.01	.769	−2.37	1.77	−.05
Anxiety	I	Pretest	Posttest	3.07a	.767	<.001	1.50	4.64	.73
		Pretest	Follow up	2.83a	.958	.006	0.87	4.79	.54
		Posttest	Follow up	−.23	.895	.796	−2.06	1.60	−.05
	C	Pretest	Posttest	.63	.756	.409	−.91	2.18	.15
		Pretest	Follow up	.43	.727	.556	−1.05	1.92	.11
		Posttest	Follow up	−.20	.806	.806	−1.85	1.45	−.05
Stress	I	Pretest	Posttest	7.00a	.964	<.001	5.03	8.97	1.33
		Pretest	Follow up	6.00a	1.19	<.001	3.55	8.45	.91
		Posttest	Follow up	−1.00	1.08	.365	−3.22	1.22	−.17
	C	Pretest	Posttest	1.53	.957	.120	−.42	3.49	.29
		Pretest	Follow up	1.33	.999	.192	−.71	3.38	.24
		Posttest	Follow up	−.20	1.03	.848	−2.32	1.92	−.04
Resilience	I	Pretest	Posttest	−1.07a	.148	<.001	−1.37	−1.08	−1.32
		Pretest	Follow up	−.93a	.162	<.001	−1.26	−1.78	−1.05
		Posttest	Follow up	.14	.191	.467	−0.25	−.598	.13
	C	Pretest	Posttest	−.17	.109	.129	−.39	.004	−.29
		Pretest	Follow up	−.12	.110	.284	−.35	.006	−.20
		Posttest	Follow up	.05	.142	.728	−.24	−.006	.06

Note. Pairwise comparisons of estimated marginal means based on the original scale of dependent variable depression; I = Intervention; C = Control. a. The mean difference is significant at the .05 level.

With respect to stress, substantial reductions were evident in the intervention group. Scores decreased significantly from pretest to posttest (M_diff = 7.00, SE = .96, p < .001, 95% CI [5.03, 8.97], d = 1.33) and from pretest to follow-up (M_diff = 6.00, SE = 1.19, p < .001, 95% CI [3.55, 8.45], d = .91). However, the change between posttest and follow-up did not reach statistical significance (M_diff = −1.00, SE = 1.08, p = .365, 95% CI [−3.22, 1.22]), indicating stability of improvement. The control group, by comparison, did not show significant changes across any measurement points (all p > .05). Improvements in resilience further supported the effectiveness of the intervention. The intervention group showed a significant increase from pretest to posttest (M_diff = −1.07, SE = .15, p < .001, 95% CI [−1.37, −0.77], d = −1.32) and from pretest to follow-up (M_diff = −0.93, SE = .16, p < .001, 95% CI [−1.26, −0.60], d = −1.05). The posttest to follow-up change was not significant (M_diff = 0.14, SE = .19, p = .467, 95% CI [−0.25, 0.53]), indicating that gains were sustained rather than further increased. In contrast, resilience scores in the control group remained largely unchanged over time (all p > .05). The pairwise comparisons across time points were conducted to further examine within- and between-group changes. These analyses supported the primary GEE results.

Discussion

This randomized controlled trial evaluated the efficacy of SBCBT in improving psychological well-being among university students. Consistent with our hypotheses, participation in SBCBT produced substantial reductions in DASS-21, alongside significant improvements in resilience. Baseline equivalence across all measures confirmed the rigor of our randomization, allowing us to confidently attribute the observed changes to the intervention. These positive effects were evident immediately following the intervention and were largely maintained at the one-month follow-up, whereas the control group, which received only self-help materials, showed no meaningful change. Collectively, these findings provide strong evidence that SBCBT addresses both the reduction of psychological distress and the cultivation of adaptive psychological resources in vulnerable student populations.

The current findings both align with and extend the existing literature on mental health interventions for students. Recent meta-analyses of digital and in-person CBT programs for university students have traditionally shown small to moderate improvements in depression and anxiety (Li et al., 2025; Kambeitz-Ilankovic et al., 2022). In contrast, the effect sizes observed in this trial were exceptionally large (e.g., Cohen's d > 1.0 for stress reduction and resilience enhancement). This magnitude of change indicates that explicitly incorporating strengths-based strategies within a traditional CBT framework may synergistically enhance therapeutic effectiveness. This support calls for “next-generation” process-based CBT approaches that focus on resilience, coping self-efficacy, and positive adaptation, in addition to merely reducing symptoms (Çalışkan & Gökkaya, 2025).

The findings on resilience further contextualize these results. Systematic reviews indicate that resilience-focused interventions in higher education can enhance coping confidence and well-being, although effects often attenuate after a few weeks (Abulfaraj et al., 2024). For instance, O’Connor and colleagues’ RISE program improved coping confidence but produced only modest reductions in psychological distress. In contrast, SBCBT in the present trial simultaneously yielded clinically meaningful improvements in resilience and robust reductions in DASS-21. This suggests that weaving resilience-building strategies into the CBT framework produces broader and more durable benefits than resilience-only programs. Evidence from strengths-oriented approaches also corroborates our results. Smith et al. (2025) found that strengths-based group interventions improved students’ resilience and self-discovery, while Murphy (2023) demonstrated that spiritually adapted CBT enhanced hope and psychological well-being in clinical populations. By extending such approaches to a nonclinical academic setting, our findings highlight the versatility of SBCBT adaptations across populations and contexts.

The observed concurrent decline in distress and rise in resilience aligns with theoretical models such as broaden-and-build theory, which proposes that cultivating positive psychological resources widens coping repertoires and fosters long-term well-being. Process-based CBT and foundational SBCBT models (e.g., Padesky & Mooney, 2012) similarly emphasize tailoring interventions to mechanisms such as self-efficacy and strengths use, providing a conceptual framework for understanding SBCBT's dual effects. Psychometrically, the increases in resilience measured by the BRS are consistent with recent multicountry studies, which demonstrate the scale's sensitivity to change and measurement invariance across cultures (Zhou & Ma, 2026). Likewise, the reductions observed on the DASS-21 subscales align with recent cross-cultural validation studies, which confirm its reliability in diverse student populations (Bonifacci et al., 2024).

Theoretical and Practical Implications

The findings are theoretically significant as they support the broaden-and-build model, which posits that positive emotions and strengths expand cognitive and behavioral repertoires, thereby facilitating resilience. By guiding students to identify personal strengths, construct personal models of resilience, and apply them to real-life challenges, SBCBT enhances coping self-efficacy and fosters adaptive behavioral flexibility, key transdiagnostic mechanisms in process-based CBT. Practically, the results have direct implications for campus mental health services. These findings have important implications for university counseling services and social work practitioners working with student populations. The SBCBT can be delivered efficiently in group formats, making it a scalable and resource-sensitive intervention for universities facing an increasing demand for psychological support. Importantly, the intervention's dual effect, which reduces distress and strengthens resilience, suggests it could serve both preventive and therapeutic functions. The attenuation of gains at follow-up underscores the need for booster sessions or integration with digital maintenance tools to ensure long-term sustainability.

Strengths and Limitations

This study possesses several notable strengths, including its randomized controlled design, the use of validated outcome measures, and the simultaneous assessment of both distress and resilience trajectories. By integrating resilience-enhancing components within a CBT framework, the intervention extends beyond a narrow focus on symptom reduction and instead demonstrates a dual-action model that targets both psychological vulnerability and protective factors. The theoretically grounded nature of the intervention, combined with a rigorous analytic approach using GEE that appropriately account for missing data, further strengthens the robustness of the findings. Moreover, the inclusion of both negative emotional outcomes and resilience indicators offers a more comprehensive evaluation compared to symptom-focused investigations alone.

Despite these strengths, several limitations warrant consideration. First, the relatively homogeneous sample, drawn from a single academic setting, may have contributed to the high treatment fidelity and substantial effect sizes observed, thereby limiting the generalizability of the findings to more diverse populations and individuals with more severe psychological difficulties. Second, the follow-up period was limited to one month, restricting conclusions regarding the long-term maintenance of treatment gains. Third, the study did not employ an active comparison condition. Consequently, the observed benefits of SBCBT may have been influenced, at least in part, by nonspecific therapeutic factors, such as therapist attention, treatment expectancy, group interaction, and participant engagement. Future trials should incorporate a more rigorous comparison condition that is perceived as equally credible by participants and requires a comparable level of time, effort, and therapeutic contact as the SBCBT intervention. Such an approach would provide a more stringent test of the specific efficacy of SBCBT and strengthen causal inferences regarding treatment effects. In addition, treatment fidelity and therapist competence were not formally evaluated, which may limit confidence in the consistency and replicability of intervention delivery. Future research should extend follow-up assessments to at least 3–6 months to evaluate the durability of treatment outcomes. Mechanistic investigations are also warranted to examine whether improvements in resilience and coping self-efficacy mediate reductions in psychological distress. Finally, evaluating SBCBT across culturally diverse populations and exploring hybrid or fully digital delivery formats would help establish its scalability, adaptability, and broader external validity.

Conclusion

This trial provides compelling evidence that SBCBT is an effective, mechanism-aligned intervention for enhancing the psychological well-being of university students. The intervention produced large and clinically meaningful reductions in DASS-21, accompanied by substantial gains in resilience that persisted at one-month follow-up. This dual action alleviating psychopathology while simultaneously fostering adaptive psychological resources addresses both vulnerability and protection, thereby advancing current models of student mental-health intervention.

Importantly, the findings highlight that integrating strengths-based strategies into conventional CBT frameworks may enhance therapeutic potency beyond the effects typically observed in standard or digital-only CBT programs. The observed concurrent improvement in resilience and reduction in distress is consistent with broaden-and-build theory and process-based CBT models, which emphasize the centrality of self-efficacy and resource activation as drivers of enduring change. From a practical perspective, these results underscore the potential of SBCBT to be implemented within university counseling services and preventive mental-health programs as a scalable, low-stigma, and high-impact intervention. Embedding resilience-oriented CBT modules into student support systems could provide a first-line or stepped-care option, particularly during periods of heightened academic stress.

Future research should extend these findings by examining longer-term trajectories, testing active comparators, and exploring hybrid delivery formats (e.g., digital boosters or group-based modules) to optimize scalability and sustainability. Taken together, the evidence positions SBCBT as a next-generation intervention that not only mitigates psychological distress but also equips young adults with the enduring psychological strengths required to thrive in challenging academic and life contexts.

Footnotes

ORCID iDs

Asma Perveen

Shahla Ostovar

Saeid Motevalli

Abbas Abdollahi

Ika Rahma Susilawati

Ethical Approval

This study involving human participants was conducted in accordance with all applicable institutional guidelines and received approval from the Institutional Review Board of Universiti Pendidikan Sultan Idris (UPSI; No: RIMC/2024-0415-01). Documentation of the ethical approval can be obtained from the corresponding and primary authors [AP] upon reasonable request.

Consent to Participate

Informed consent was obtained from all participants prior to their involvement in the study. Participation was voluntary, and individuals were clearly informed that they could withdraw at any stage without any consequences. Copies of the signed consent forms are available from the corresponding author upon reasonable request.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability

The datasets generated and analyzed during the current study are available from the corresponding and primary authors [AP] upon reasonable request.

References

Abulfaraj

G. G.

Upsher

Zavos

H. M. S.

Dommett

E. J.

(2024). The impact of resilience interventions on university Students’ mental health and well-being: A systematic review. Education Sciences, 14(5), 510. https://doi.org/10.3390/educsci14050510

Arslan

Aydoğdu

Uzun

(2025). Longitudinal impact of the ACT-based positive psychology intervention to improve happiness, mental health, and well-being. Psychiatric Quarterly, 96(1), 847–868. https://doi.org/10.1007/s11126-025-10145-7

Barbayannis

Bandari

Zheng

Baquerizo

Pecor

K. W.

Ming

(2022). Academic stress and mental well-being in college students: Correlations, affected groups, and COVID-19. Frontiers in Psychology, 13(1), 886344. https://doi.org/10.3389/fpsyg.2022.886344

Beiter

Nash

McCrady

Rhoades

Linscomb

Clarahan

Sammut

(2015). The prevalence and correlates of depression, anxiety, and stress in a sample of college students. Journal of Affective Disorders, 173, 90–96. https://doi.org/10.1016/j.jad.2014.10.054

Bonifacci

Borghetti

Cangelosi

(2024). Parents’ and children’s emotional well-being and language beliefs in heritage bilingual families. European Journal of Investigation in Health, Psychology and Education, 14(9), 2509–2522. https://doi.org/10.3390/ejihpe14090166

Çalışkan

Gökkaya

(2025). Reviewing psychological practices to enhance the psychological resilience process for individuals with chronic pain: Clinical implications and neurocognitive findings. Current Pain and Headache Reports, 29(1), 68. https://doi.org/10.1007/s11916-025-01373-4

Duffy

Keown-Stoneman

Goodday

Horrocks

Lowe

King

Pickett

McNevin

S. H.

Cunningham

Rivera

Bisdounis

Bowie

C. R.

Harkness

Saunders

K. E. A.

(2020). Predictors of mental health and academic outcomes in first-year university students: Identifying prevention and early-intervention targets. British Journal of Psychiatry, 6(3), e46. https://doi.org/10.1192/bjo.2020.24

Durlak

J. A.

Weissberg

R. P.

Dymnicki

A. B.

Taylor

R. D.

Schellinger

K. B.

(2011). The impact of enhancing students’ social and emotional learning: A meta-analysis of school-based universal interventions. Child Development, 82(1), 405–432. https://doi.org/10.1111/j.1467-8624.2010.01564.x

Eisenberg

Gollust

S. E.

Golberstein

Hefner

J. L.

(2007). Prevalence and correlates of depression, anxiety, and suicidality among university students. American Journal of Orthopsychiatry, 77(4), 534–542. https://doi.org/10.1037/0002-9432.77.4.534

10.

Faul

, et al. (2007). G*power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146

11.

Fergus

Zimmerman

M. A.

(2005). Adolescent resilience: A framework for understanding healthy development in the face of risk. Annual Review of Public Health, 26(1), 399–419. https://doi.org/10.1146/annurev.publhealth.26.021304.144357

12.

Gao

Hashim

I. H. M.

Ostovar

(2025). Profiles of savoring beliefs and their associations with savoring strategies and subjective well-being: A latent profile analysis. Personality and Individual Differences, 246(1), 113373. https://doi.org/10.1016/j.paid.2025.113373

13.

Gao

Hashim

I. H. M.

Ostovar

(2025). Savoring promotes a balanced time perspective and subjective well‐being among university students: Evidence from a cross‐sectional study and an intervention study. Applied Psychology: Health and Well-Being, 17(3), e70039. https://doi.org/10.1111/aphw.v17.3

14.

Hartley

M. T.

(2011). Examining the relationships between resilience, mental health, and academic persistence in undergraduate college students. Journal of American College Health, 59(7), 596–604. https://doi.org/10.1080/07448481.2010.515632

15.

Hofmann

S. G.

Asnaani

Vonk

I. J.

Sawyer

A. T.

Fang

(2012). The efficacy of cognitive behavioral therapy: A review of meta-analyses. Cognitive Therapy and Research, 36(5), 427–440. https://doi.org/10.1007/s10608-012-9476-1

16.

Hunt

Eisenberg

(2010). Mental health problems and help-seeking behavior among college students. Journal of Adolescent Health, 46(1), 3–10. https://doi.org/10.1016/j.jadohealth.2009.08.008

17.

Hyseni Duraku

Davis

Hamiti

(2023). Mental health, study skills, social support, and barriers to seeking psychological help among university students: A call for mental health support in higher education. Frontiers in Public Health, 11(1), 1220614. https://doi.org/10.3389/fpubh.2023.1220614

18.

Infurna

F. J.

Jayawickreme

Woods-Jaeger

Zalta

A. K.

(2024). Understanding adaptive responses to adversity: Introduction to the special issue on rethinking resilience and posttraumatic growth. The American Psychologist, 79(8), 989–998. https://doi.org/10.1037/amp0001442

19.

Kambeitz-Ilankovic

Rzayeva

Völkel

Wenzel

Weiske

Jessen

Reininghaus

Uhlhaas

P. J.

Alvarez-Jimenez

Kambeitz

(2022). A systematic review of digital and face-to-face cognitive behavioral therapy for depression. NPJ Digital Medicine, 5(1), 144. https://doi.org/10.1038/s41746-022-00677-8

20.

Kirkbride

J. B.

Anglin

D. M.

Colman

Dykxhoorn

Jones

P. B.

Patalay

Pitman

Soneson

Steare

Wright

Griffiths

S. L.

(2024). The social determinants of mental health and disorder: Evidence, prevention and recommendations. World Psychiatry, 23(1), 58–90. https://doi.org/10.1002/wps.21160

21.

Larcombe

Finch

Sore

Murray

C. M.

Kentish

Mulder

R. A.

Lee-Stecum

Baik

Tokatlidis

Williams

D. A.

(2016). Prevalence and socio-demographic correlates of psychological distress among students at an Australian university. Studies in Higher Education, 41(6), 1074–1091. https://doi.org/10.1080/03075079.2014.966072

22.

Zhang

(2025). The intervention effect of long-term exercise on depression and anxiety in college students: A three-level meta-analysis. BMC Psychology, 13(1), e0239429. https://doi.org/10.1186/s40359-025-03009-z

23.

Lovibond

S. H.

Lovibond

P. F.

(1995). Depression Anxiety Stress Scales (DASS–21, DASS–42) [Database record]. APA PsycTests. https://doi.org/10.1037/t01004-000

24.

Martell

C. R.

Dimidjian

Herman-Dunn

(2010). Behavioral activation for depression: A clinician’s guide. The Guilford Press.

25.

Masten

A. S.

(2014). Ordinary magic: Resilience in development. Guilford Press.

26.

Mofatteh

(2021). Risk factors associated with stress, anxiety, and depression among university undergraduate students. AIMS Public Health, 8(1), 36–65. https://doi.org/10.3934/publichealth.2021004

27.

Murphy

E. R.

(2023). Hope and well-being. Current Opinion in Psychology, 50(1), 101558. https://doi.org/10.1016/j.copsyc.2023.101558

28.

Padesky

C. A.

Mooney

K. A.

(2012). Strengths-based cognitive-behavioural therapy: A four-step model to build resilience. Clinical Psychology & Psychotherapy, 19(4), 283–290. https://doi.org/10.1002/cpp.1795

29.

Pan

J. Y.

Zhuang

(2024). Adventure-Based cognitive behavioral intervention for Hong Kong university students: A randomized controlled study. Research on Social Work Practice, 34(2), 144–157. https://doi.org/10.1177/10497315231163804

30.

Rashid

(2015). Positive psychotherapy: A strength-based approach. The Journal of Positive Psychology, 10(1), 25–40. https://doi.org/10.1080/17439760.2014.920411

31.

Schlack

Peerenboom

Neuperdt

Junker

Beyer

A. K.

(2021). The effects of mental health problems in childhood and adolescence in young adults: Results of the KiGGS cohort. Journal of Health Monitoring, 6(4), 3–19. https://doi.org/10.25646/8863

32.

Slade

(2010). Mental illness and well-being: The central importance of positive psychology and recovery approaches. BMC Health Services Research, 10(1), 26. https://doi.org/10.1186/1472-6963-10-26

33.

Smith

B. W.

Dalen

Wiggins

Tooley

Christopher

Bernard

(2008). The Brief Resilience Scale: Assessing the ability to bounce back. International Journal of Behavioral Medicine, 15(3), 194–200. https://doi.org/10.1080/10705500802222972

34.

Smith

G. D.

Watson

Poon

Cross

W. M.

Hutchison

M. L.

Jacob

M. A.

Rahman

M. A.

Penny

Lam

(2025). A study of academic resilience, psychological resilience and psychological distress in undergraduate nursing students in Hong Kong and Australia during COVID-19 restrictions. Nurse Education in Practice, 84(1), 104331. https://doi.org/10.1016/j.nepr.2025.104331

35.

Sonbol

H. M.

Metwally El-Sayed

Taha

S. M.

Abdelwahab Khedr

El-Ashry

A. M.

Abd Elhay

E. S.

(2025). Effectiveness of a spiritual adaptation of cognitive behavioural therapy in improving resilience, self-esteem and spirituality among clients with opioid use disorder: A quasi-experimental study. Journal of Psychiatric and Mental Health Nursing, 32(1), 112–124. https://doi.org/10.1111/jpm.13089

36.

Stallman

H. M.

(2010). Psychological distress in university students: A comparison with general population data. Australian Psychologist, 45(4), 249–257. https://doi.org/10.1080/00050067.2010.482109

37.

Taylor

R. D.

Oberle

Durlak

J. A.

Weissberg

R. P.

(2017). Promoting positive youth development through school-based social and emotional learning interventions: A meta-analysis of follow-up effects. Child Development, 88(4), 1156–1171. https://doi.org/10.1111/cdev.12864

38.

Troy

A. S.

Willroth

E. C.

Shallcross

A. J.

Giuliani

N. R.

Gross

J. J.

Mauss

I. B.

(2023). Psychological resilience: An affect-regulation framework. Annual Review of Psychology, 74(1), 547–576. https://doi.org/10.1146/annurev-psych-020122-041854

39.

Ungar

(2011). The social ecology of resilience: Addressing contextual and cultural ambiguity of a nascent construct. American Journal of Orthopsychiatry, 81(1), 1–17. https://doi.org/10.1111/j.1939-0025.2010.01067.x

40.

Wainberg

M. L.

Scorza

Shultz

J. M.

Helpman

Mootz

J. J.

Johnson

K. A.

Neria

Bradford

J. E.

Oquendo

M. A.

Arbuckle

M. R.

(2017). Challenges and opportunities in global mental health: A research-to-practice perspective. Current Psychiatry Reports, 19(5), 28. https://doi.org/10.1007/s11920-017-0780-z

41.

Wang

Feng

(2022). A narrative review of empirical literature of behavioral activation treatment for depression. Frontiers in Psychiatry, 13(1), 845138. https://doi.org/10.3389/fpsyt.2022.845138

42.

Xiang

Wan

Zhu

(2025). Effects of cognitive behavioral therapy on resilience among adult cancer patients: A systematic review and meta-analysis. BMC Psychiatry, 25(1), 204. https://doi.org/10.1186/s12888-025-06628-3

43.

Xing

Luo

(2023). Psychological resilience interventions for adolescents during the COVID-19 pandemic. Behavioral Sciences (Basel, Switzerland), 13(7), 543. https://doi.org/10.3390/bs13070543

44.

Yuen

Sadhu

Pfeffer

Sarvet

Daily

R. S.

Dowben

Jackson

Schowalter

Shapiro

Stubbe

, & Group for the Advancement of Psychiatry (GAP) Child Committee. (2020). Accentuate the positive: Strengths-based therapy for adolescents. Adolescent Psychiatry (Hilversum, Netherlands), 10(3), 166–171. https://doi.org/10.2174/2210676610666200225105529

45.

Zhou

(2026). Evaluating the brief resilience scale in 21 countries: Psychometric properties and measurement invariance across sex, age, and countries. Assessment, 33(5), 765–783. https://doi.org/10.1177/10731911251355124