Depressive symptoms in higher education students during the COVID-19 pandemic: the role of containment measures
Bibliographic record
Abstract
BACKGROUND: Students are a vulnerable group for the indirect impact of the COVID-19 pandemic, particularly their mental health. This paper examined the cross-national variation in students' depressive symptoms and whether this can be related to the various protective measures implemented in response to the initial stage of the COVID-19 outbreak. METHODS: Student data stem from the COVID-19 International Student Well-being Study, covering 26 countries during the first wave of the COVID-19 pandemic. Country-level data on government responses to the COVID-19 pandemic were retrieved from the Oxford COVID-19 Tracker. Multilevel analyses were performed to estimate the impact of the containment and economic support measures on students' depressive symptoms (n = 78 312). RESULTS: School and workplace closures, and stay-at-home restrictions were positively related to students' depressive symptoms during the COVID-19 pandemic, while none of the economic support measures significantly related to depressive symptoms. Countries' scores on the index of these containment measures explained 1.5% of the cross-national variation in students' depressive symptoms (5.3%). This containment index's effect was stable, even when controlling for the economic support index, students' characteristics, and countries' epidemiological context and economic conditions. CONCLUSIONS: Our findings raise concerns about the potential adverse effects of existing containment measures (especially the closure of schools and workplaces and stay-at-home restrictions) on students' mental health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".