Depression, Anxiety and Stress Symptomatology among Swedish University Students Before and During the COVID-19 Pandemic: A Cohort Study.
Bibliographic record
Abstract
Abstract Background. The COVID-19 pandemic has had a profound effect on societies, economies, and daily life of citizens worldwide. This has raised important concerns about the mental health of different populations. We aimed to determine if symptom levels of depression, anxiety, and stress were different during the COVID-19 outbreak compared to before, with the Depression, Anxiety and Stress Scale as main outcome. We also aimed to determine whether pre-pandemic loneliness, poor sleep quality and mental health problems were associated with worse trajectories of mental health. Methods. We conducted a cohort study with 1658 Swedish university students answering questionnaires before the pandemic and a 81 % response-rate to follow-ups during the pandemic. Generalized Estimating Equations were used to estimate mean levels of symptoms before and during the pandemic, and to estimate effect modification by levels of loneliness, sleep quality and pre-existing mental health problems. Results. We found small differences in symptoms. Mean depression increased by 0.23/21 (95% CI:0.03 to 0.43), mean anxiety decreased by -0.06/21 (95% CI: -0.21 to 0.09) and mean stress decreased by - 0.34/21 (95% CI: -0.56 to -0.12). Loneliness, poor sleep quality and pre-existing mental health problems minimally influenced trajectories. Conclusions. Contrary to widely held concerns, we found minimal changes in mental health among Swedish university students during the first months of the COVID-19 pandemic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".