Depression, anxiety and stress among Swedish university students before and during six months of the COVID-19 pandemic: A cohort study
Bibliographic record
Abstract
AIMS: The COVID-19 pandemic has had a profound effect on societies and citizens worldwide, raising concerns about potential mental health impacts. We aimed to describe trajectories of depression, anxiety and stress symptoms during the COVID-19 outbreak compared to before the outbreak, and to determine if trajectories were modified by pre-pandemic loneliness, poor sleep quality and mental health problems. METHODS: We conducted a cohort study with 1836 Swedish university students entering the study before 13 March 2020, the onset of the pandemic, with follow-ups within three (FU1) and six months (FU2) of the outbreak. Generalized Estimating Equations were used to estimate mean differences in symptom levels over time-periods, and to estimate potential effect modifications. RESULTS: We found small differences in mean levels of the depression, anxiety and stress scale (DASS-21) over time. Compared to before the pandemic, depression increased by 0.25 points of 21 (95% CI: 0.04 to 0.45) at FU1 and decreased by 0.75/21 (95% CI:-0.97 to -0.53) at FU2. Anxiety decreased from baseline to FU1 by 0.09/21 (95% CI: -0.24 to 0.07) and by 0.77/21 (95% CI: -0.93 to -0.61) to FU2. Stress decreased from baseline to FU1 by 0.30/21 (95% CI: -0.52 to -0.09) and by 1.32/21 (95% CI: -1.55 to -1.09) to FU2. Students with pre-pandemic loneliness, poor sleep quality or pre-pandemic mental health problems did not have worse trajectories of mean mental health symptoms. CONCLUSIONS: Symptom levels were relatively stable during the first three months of the pandemic, while there was a slight decrease during the summer months, probably due to seasonality effects.
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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".