Depression, anxiety and stress among Swedish university students during the second and third waves of COVID-19: A cohort study
Bibliographic record
Abstract
Our research group recently reported that symptom levels of depression, anxiety, and stress among Swedish university students were relatively stable during the first three months of COVID-19 and decreased slightly during the following three summer months. Since then, Sweden has faced a second and third wave of COVID-19. Aims: This study aims to describe the mean trajectories of depression, anxiety and stress symptoms among Swedish university students before and during the second and third waves of the COVID-19 pandemic. Methods: We recruited 1835 participants in September 2020, of whom 81% provided follow-ups in December 2020–January 2021 and 77% provided follow-ups in March–April 2021. The short-form Depression, Anxiety and Stress Scale was used to measure mental health symptoms. Generalized estimating equations were used to estimate the mean differences in symptom levels over the three time periods. Results: Compared with September, mean depression was 0.91 points of 21 higher (95% confidence interval (CI) 0.70–1.13) in December 2020–January 2021 and 0.66 points higher (95% CI 0.43–.88) in March–April 2021. Anxiety levels were 0.20 points higher (95% CI 0.05–0.34) in December 2020–January 2021 and 0.17 points higher (95% CI 0.02–0.33) in March–April 2021. Stress levels were 0.21 points higher (95% CI 0.00–0.41) in December 2020–January 2021 and 0.16 points lower (95% CI −0.38 to 0.05) in March–April 2021. Conclusions: Our results indicate relatively stable levels of mental health among Swedish university students during the second and third waves of COVID-19 compared with before the second wave. Mean depression symptom scores increased slightly, but the importance of this small increase is uncertain.
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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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".