Table 2. Direct and Indirect Effects from the Structural Equation Modeling Analysis

The model explains 0.7% of coping, 26.3% of resilience, and 4.7% of GPA variance. While distress and coping predict resilience, they explain little of academic performance and coping, suggesting other factors remain unmeasured and warrant further exploration.

Figure 3. Structural Equation Model Demonstrating the Relationships Between Distress, Coping, Resilience and Academic Performance

Standardised path coefficients are displayed along each arrow. GPA represents academic performance. Distress negatively predicted both resilience (β = -0.34) and GPA (β = -0.19) and had a small positive effect on coping (β = 0.08). Coping was positively associated with resilience (β = 0.41) and GPA (β = 0.18), while resilience negatively predicted GPA (β = -0.15). Arrow thickness reflects the strength of the relationship. All coefficients shown are statistically significant (p < .05). GPA = grade point average. Note that indirect effects are not shown.

F. Psychological Influences on Academic Performance are Stable Across Semesters

A multi-group SEM was conducted using semester as the grouping variable to examine whether the structural relationships in the SEM differed across academic stages. A constrained model was compared to an unconstrained model. This approach allowed us to compare whether the groups’ structural pathways were statistically similar. The chi-square difference test revealed no statistically significant difference between the two models, χ² (18) = 26.75, p = .084. We can conclude that the relationships between psychological predictors and academic performance remained relatively stable throughout the different stages of medical training.

IV. DISCUSSION

This study explored the relationship between psychological distress, coping, resilience, and academic performance among medical students, as well as how these constructs evolved across different stages of training using a pseudo-longitudinal approach. To examine temporal trends, we conducted ANCOVA to compare key variables across academic semesters. We then applied PCA to identify underlying latent constructs, followed by SEM to assess these psychosocial factors’ direct and indirect effects on academic performance. While our findings provide insights into the psychological well-being of medical students, the cross-sectional nature of this study limits our ability to draw causal relationships among distress, coping, resilience, and academic performance.

ANCOVA revealed that psychological distress (DASS-21) and burnout (SBI) varied by semester, following a U-shaped pattern. Distress was lowest in Semester 1, peaked in Semesters 3 and 5, then subsided by Semester 7, reflecting mid‐phase stress during the shift from basic sciences to complex integrated human systems, consistent with prior reports of heightened stress at major curricular transitions (Boni et al., 2018; Hansell et al., 2019; Prendergast et al., 2024; Voltmer et al., 2021). Our results reinforce the importance of implementing preventive mental health measures early in the curriculum to support students during this transition.
Positive resilience traits, including perseverance and help-seeking, declined across semesters and did not recover by Semester 7, unlike distress and burnout. This suggests a lasting impact, likely due to early academic stress. Resilience is known to be dynamic and sensitive to environmental stressors (Köhne et al., 2023; Ollis et al., 2022; Thompson et al., 2016). Wang et al. (2022) longitudinal analysis identified a bidirectional relationship between resilience and burnout, showing that high burnout can degrade resilience over time and vice versa. Our findings support the idea that early distress may weaken resilience, though causality cannot be confirmed due to the study’s cross-sectional design. Perceptions of the learning environment declined steadily across semesters, with Semester 1 students reporting the most positive views. This may reflect rising stress or disillusionment. Prior studies link poor learning climate to burnout, lower academic performance, and reduced quality of life (Edgerton & McKechnie, 2023; Esquerda et al., 2024; Shahzad & Wajid, 2024), highlighting the need for targeted support strategies.

Unlike resilience, coping styles remained stable throughout the semester, supporting their view as enduring personality traits (Kardum & Krapić, 2001). However, avoidant coping was more common in students with mental health diagnoses, chronic illness, or alcohol use, supporting earlier findings linking avoidant strategies with poor psychological outcomes (Thompson et al., 2016; Villasana et al., 2016; Wang et al., 2022). These associations suggest that although coping styles are stable, they may become independent risk factors over time. Since resilience is environmentally influenced and were shown to be modifiable (Rosas-Santiago, 2019), targeting maladaptive coping early may be a viable preventive action. Identifying avoidant patterns and offering structured interventions, especially during curricular transitions, could support well-being, resilience, and academic success.

We employed PCA to combine the nine variables to reduce redundancy and increase stability. PCA analysis identified three main components that explained 74.3% of the total variance. The three interpretable components are distress, coping, and resilience. The conceptual grouping is grounded in established theoretical frameworks and supported by statistical analysis. The distress component, comprising psychological distress, burnout, negative affect, and avoidant coping, captures a multidimensional construct of distress as interactions between demands and coping resources, aligning with Lazarus and Folkman’s transactional model (Folkman, 2013). Avoidant coping and negative affect were factors affecting and causing distress (Holahan et al., 2005; Kardum & Krapić, 2001; Thompson et al., 2016), reinforcing their role as core components.

The resilience component, perseverance, adaptive help-seeking, and perceptions of the learning environment reflect resilience as a positive resource that interacts with students’ perceptions within their learning environment. This aligns with models that conceptualise resilience not as a fixed trait but as a dynamic construct shaped by both the individual and the social and environmental context they are currently in (Béné et al., 2016; Waxman et al., 1997). The coping component, emotion-focused and problem-focused strategies, captures the adaptive behavioral and cognitive responses to stress (Kardum & Krapić, 2001; Thompson et al., 2016). This contrasts with avoidant coping, which was viewed as maladaptive and had a higher association with distress (Holahan et al., 2005). According to Lazarus and Folkman’s model, these strategies serve as the primary means through which individuals attempt to manage stress and maintain psychological equilibrium (Folkman, 2013). The constructs we defined were statistically distinct and theoretically sound, supporting their use as latent constructs in subsequent SEM analysis.

Structural equation modeling supported the framework linking distress, coping, and resilience to academic performance. In line with Lazarus and Folkman’s model (Folkman, 2013), distress negatively affected GPA directly and indirectly via reduced resilience. This highlights how psychological burden can impair concentration and motivation (Almarzouki, 2024). The model confirmed resilience as a buffer that mitigates distress’s impact on performance, aligning with studies showing its protective role against burnout and stress both in academic and work contexts (de la Fuente et al., 2021; Herrero & Díaz, 2025, 2025; Zhang et al., 2024, 2024; Zhu et al., 2024, 2024). While coping had a marginal indirect effect, its strong link to resilience suggests that enhancing adaptive coping may improve both resilience and academic outcomes. Promoting effective coping as an intervention target may boost resilience and, in turn, academic performance.

An unexpected finding of this study was that resilience negatively predicted GPA, a result that contrasts with the literature suggesting resilience supports academic success (Calo et al., 2019, 2024; Ho & Kwek, 2022; Jumat et al., 2020; Sanjaya et al., 2024). Possible explanations include measurement artifacts due to how resilience was defined and its overlap with burnout, and reference bias in self‐ratings. West et al. showed that students in demanding environments judge non‐cognitive traits like grit using their peers as standards, which can obscure their relationship with GPA (West et al., 2016). Future studies should account for this bias by using methods like external observer ratings or vignettes to improve measurement validity.

Although most SEM effect sizes were small, they were significant and consistent with prior research linking distress, resilience, and coping to student well-being and performance. Small effects, when sustained or combined with other stressors, can undermine engagement and outcomes. This highlights the need for early psychological interventions to prevent cumulative impacts throughout medical school. Given the fluctuating mean levels across semesters, we conducted a multi-group SEM using semester as the grouping variable to test whether relationships among distress, coping, resilience, and performance varied across training stages. The non-significant chi-square difference indicates stable structural pathways, suggesting that interventions targeting coping and resilience may be effective across all semesters.

This study helps us understand how distress, coping, and resilience relate to academic performance throughout medical school. One of its strengths is its local setting in Indonesia, offering insight into how unique factors in Southeast Asia, like education systems, cultural expectations, and health care environments, shape students’ mental health and academic experience. These findings add to the limited research from the Asia-Pacific region and point to the need for tailored support and policies.

There are some limitations to this study. Other important factors that could affect stress and performance were not measured. Students’ sense of belonging, which recent studies have shown plays a key role in helping students stay engaged, avoid burnout, and do well academically, was not measured (Aker & Şahin, 2022; Leep Hunderfund et al., 2025). Similarly, impostor syndrome, which affects up to half of medical students (Sotiropoulos, 2021; Villwock et al., 2016) and is linked to burnout, low self-esteem, and poorer academic outcomes (Diaconescu et al., 2024; Faizan Siddiqui & Azaroual, 2024; Villwock et al., 2016), was not assessed. The absence of these constructs may account for unexplained variance in our models and represents important directions for future research.

Additionally, while the tuition source was collected, it was not analysed as a proxy for socio-economic status, and we acknowledge that it may not fully reflect students’ financial circumstances. This is a notable limitation, as socioeconomic status has been consistently linked to academic success (Tan, 2024). Future studies should incorporate measurements, such as parental income or education.

Other limitations regarding the study design are also acknowledged. First, the cross-sectional design of our study prevents causal inferences regarding the relationships among distress, coping, resilience, and academic performance. Although we used a pseudo-longitudinal approach by comparing semester groups, longitudinal data would be needed to confirm how these constructs evolve over time. Second, reliance on self-reported measures introduces potential bias, such as recall and social desirability. Psychological and behavioural data, including alcohol, smoking, and mental health history, were self-reported, which may lead to underreporting, especially among those with higher use (Davis et al., 2010). Future studies should consider objective or multi-source reporting. Third, although PCA and SEM reduced redundancy and improved model stability, they may oversimplify complex psychological constructs. The relatively low explained variance in GPA also suggests other key predictors were not captured. Finally, as this was a single-institution study, generalisability is limited. Future research should adopt a multi-center, longitudinal design to improve validity and capture differences across educational and cultural contexts in the Asia-Pacific region.

V. CONCLUSION

This study presents a robust, theory-driven model that directly and indirectly defines how distress impacts GPA through coping and resilience. Notably, resilience declined over time and did not recover to baseline, even without high psychological stress and burnout, while coping styles remained relatively stable. This suggests that interventions should not focus solely on resilience but also on enhancing coping strategies. Embedding such interventions during academic transition periods may help preserve psychological resources and improve academic outcomes.

Notes on Contributors

Ardo Sanjaya contributed to concept, analysis, and writing. Ray Sebastian helped collect data and draft the paper. Kevin Gunawan assisted in drafting and data collection. Christian Edwin led concept, analysis, and revisions. Nathanael A. Mianto worked on analysis and revisions. Cindra Paskaria contributed to concept and revisions. All authors have read and approved the final version of the manuscript.

Ethical Approval

This study followed the Declaration of Helsinki and received ethical approval from the Research Ethics Committee of Maranatha Christian University (Approval Number: 089/KEP/VII/2024). Electronic informed consent was obtained from all participants before their inclusion, with the consent form presented at the start of the online survey. Participants’ registration numbers, names, and email addresses were initially collected to facilitate accurate data matching and communication. However, all identifiable information was removed, and the dataset was fully anonymised before analysis to ensure confidentiality. Participants were informed that their responses would remain confidential and be used exclusively for research. All data collection and analysis procedures complied with institutional and national ethical standards to safeguard participant privacy and well-being.

Data Availability

The datasets generated during the study are available from the corresponding author upon reasonable request and are subject to ethical considerations. Supplementary materials, including the complete statistical results, complementing the articles, are available on Figshare: https://doi.org/10.6084/m9.figshare.28916246 (Sanjaya, 2025).

Acknowledgement

The authors thank Maranatha Christian University for providing the facilities to create this research.

Funding

This research was supported by an Internal Research Grant from Maranatha Christian University, under grant number 020/SK/AK/UKM/III/2025.

Declaration of Interest

The authors declare no conflict of interest.

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