Prevalence of Depressive Symptoms and Associated Psychosocial Risk Factors among French University Students: the Moderating and Mediating Effects of Resilience
Bibliographic record
Abstract
University students constitute a higher risk population for mental health problems, especially for depression. Given the lack of literature addressing such issues in France, this study estimated the prevalence rates of depression, its sociodemographic correlates in French university students [FUS], and examined whether resilience resources moderated and mediated the relationship between perceived stress and depressive symptoms. Participants (N = 1435, Mage = 20.5) completed the Beck Depression Inventory (BDI-II), the Perceived Stress Scale, the Brief Resilience Scale, and a sociodemographic questionnaire. Descriptive, multiple logistic regressions, and mediation analyses were used. With respect to the BDI-II's cutoff scores, 20.3% (95% CI: 18.3 to 22.4%) and 22.8% (95% CI: 20.7 to 25.0%) were positive to moderate and severe depression, respectively. Gender and education attainment appeared to be moderate risk factors when accounting for cumulative effect of perceived stress and resilience. Resilience buffered and partially mediated the perceived stress-depression relationship. In conclusion, the prevalence of depression was higher in FUS, as reported in previous studies. The amount of academic and daily stress explained this prevalence. University students with low resilience level were more at risk. Interventions aiming to improve resilience skills could help to mitigate the negative effects of stress and to promote mental health in this population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".