European Journal of Investigation in Health, Psychology and Education

Mental health concerns are increasingly prevalent among postgraduate students, who face academic, social, and career pressures. Although research on student mental health is expanding, less is known about the psychological resources that support well-being in postgraduate learners. Meaning in life has been identified as a key psychological resource that helps individuals interpret challenges and maintain coherence and well-being, and recent research highlights the contribution of self-transcendent traits such as gratitude, forgiveness, and spirituality in fostering meaning in life. However, empirical evidence on these interrelationships remains limited. This study involves 1527 Pakistani postgraduate students (M = 795; mean age = 24.89 years) recruited through multistage random sampling from ten public universities in Punjab. Participants completed the Gratitude Questionnaire, Heartland Forgiveness Scale, Spirituality Scale, Meaning in Life Questionnaire, and Mental Health Inventory (assessing psychological well-being and psychological distress). Correlation analyses showed that gratitude, forgiveness, and spirituality were positively associated with psychological well-being and negatively associated with psychological distress. Structural equation modeling (SEM) further indicated that these traits predicted mental health both directly and indirectly, with meaning in life serving as significant partial mediator. Overall, the findings highlight the central role of meaning in life in linking self-transcendent traits to mental health among postgraduate students and suggest important implications for culturally sensitive, university-based mental health initiatives.

This study examined the mediating role of problem-solving attitudes in the relationship between catastrophic thinking and environmental awareness among university students using structural equation modeling. Two samples of undergraduate students from Al-Azhar University, Egypt, participated: a psychometric validation sample (N = 670) and a main study sample (N = 989). Participants completed three validated instruments assessing catastrophic thinking, problem-solving attitudes, and environmental awareness. Results revealed that catastrophic thinking was significantly negatively associated with environmental awareness both directly (β = −0.266) and indirectly through problem-solving attitudes (β = −0.172), with the indirect pathway accounting for approximately 39% of the total effect. The structural model demonstrated excellent fit to the data, and all hypothesized relationships were statistically significant. These findings suggest that catastrophic cognitions are associated with reduced environmental awareness both directly and through their negative relationship with problem-solving orientations that facilitate engagement with complex issues including environmental challenges. The study highlights the importance of addressing trait-level cognitive distortions alongside environmental content in education programs, as general catastrophic thinking patterns may impair environmental awareness even among students without climate-specific anxiety.

Background and Objectives: Large language models (LLMs) have demonstrated high performance on knowledge-based medical examinations but their capabilities on cognitive aptitude tests emphasizing reasoning and abstraction remain underexplored. The Test for Medical Studies (TMS), a German medical school admission test, provides a standardized framework to examine these capabilities. This study aimed to evaluate the performance and consistency of multiple LLMs on text-based and visual-analytic TMS items. Materials and Methods: Eight contemporary LLMs, comprising proprietary and open-source systems, were evaluated using a multi-run design on standardized TMS items spanning text-based and visual-analytic cognitive domains. Results: Mean accuracy remained substantially below levels typically reported for knowledge-based medical examinations, with marked performance differences between text-based and visual-analytic subtests. Open-source models performed competitively compared with proprietary systems. Inter-run reliability was heterogeneous, indicating notable variability across repeated evaluations. Conclusions: Current LLMs show limited and domain-dependent performance on cognitive aptitude tasks relevant to medical school admission. High accuracy on knowledge-based examinations does not translate into stable performance on aptitude tests emphasizing fluid intelligence. The observed modality-dependent performance patterns and inter-run variability highlight the importance of differentiated, multi-run evaluation strategies when assessing LLMs for applications in medical education.