A repeated cross-sectional analysis assessing mental health conditions of adults as per student status during key periods of the COVID-19 epidemic in France
Bibliographic record
Abstract
Previous studies have shown the negative impact of the COVID-19 epidemic on students' mental health. It is, however, uncertain whether students are really at higher risk of mental health disturbances than non-students and if they are differentially impacted by lockdown periods over time. The objective of our study was to compare the frequency of depressive symptoms, anxiety, and suicidal thoughts in students and non-students enrolled in the same study in France and during the same key periods of the COVID-19 epidemic. Using a repeated cross-sectional design, we collected data from a sample of 3783 participants in the CONFINS study during three recruitment waves between March 2020 and January 2021. Multivariate logistic regression models, adjusted for potential confounding factors, showed that students were more likely to have high scores of depressive symptoms and anxiety more frequently than non-students. These differences were particularly strong during the first (depressive symptoms: adjusted odds ratio aOR 1.59, 95% CI 1.22-2.08; anxiety: aOR 1.63, 95% CI 1.22-2.18) and second lockdowns (depressive symptoms: aOR 1.80, 95% CI 1.04-3.12; anxiety: aOR 2.25, 95% CI 1.24-4.10). These findings suggest that the restrictive measures-lockdown and curfew-have an alarmingly stronger negative impact on students than on non-students and underline the frailty of students' mental health and the need to pay greater attention to this population in this epidemic-related context.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".