Resilience, Stress, and Mental Health Among University Students: A Test of the Resilience Portfolio Model
Bibliographic record
Abstract
In recent years, post-secondary students' mental health has become an important public health concern. However, studies examining protective factors of mental health among students and during challenging times are limited. Guided by the strength-based Resilience Portfolio Model and following a group of undergraduates (N = 1004) throughout the 2020/2021 academic year, this study examined multiple domains of resilience internal assets and external resources and simultaneously tested multiple protective mechanisms for student mental health using structural equation modelling. Results provided support for insulating effects: both internal assets such as emotion regulation and external recourses such as social network supportiveness and cultural fit in university (i.e., perceived congruity between students' personal and cultural selves and their university environment) were associated with reducing academic stress which in turn promoted student mental health at the end of the academic year. There was also support for additive effects: greater cultural fit in university was also directly related to better end-of-year student mental health. As cultural fit in university was associated both directly and indirectly with student mental health, creating an inclusive university community may help reduce student academic stress, lower student psychological distress and improve student subjective well-being.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".