[Association of COVID-19 dissemination with symptoms of anxiety and depression among university students].
Bibliographic record
Abstract
BACKGROUND: Evidence about the impact of the COVID-19 pandemic on the mental health of specific subpopulations- such as university students-is needed as communities prepare for future waves. AIMS: To study the association of proximity of COVID-19 with symptoms of anxiety and depression in university students. METHODS: This trend study analyzed weekly cross-sectional surveys of probabilistic samples of students from the University of British Columbia for 13 weeks through the first wave of COVID-19. The main variable assessed was propinquity of COVID-19, defined as "knowing someone who tested positive for COVID-19", which was specified at different levels: knowing someone anywhere globally, in Canada, in Vancouver, in their course, or at home. Proximity was included in multivariable linear regressions to assess its association with primary outcomes, including 30-day symptoms of anxiety and/or depression. RESULTS: Of 1,388 respondents (adjusted response rate=50%), 5.6% knew someone with COVID-19 in Vancouver, 0.8% in their course, and 0.3% at home. Ten percent were overwhelmed and unable to access help. Knowing someone in Vancouver was associated with an 11 percentage-point increase in the probability of 30-day anxiety symptoms (SE=0,05; p≤0,05), moderated by gender, with a significant interaction of the exposure and being female (coefficient= 20(SE=0,09), p≤0,05). No association was found with depressive symptoms. CONCLUSION: Propinquity of COVID-19 cases may increase the likelihood of anxiety symptoms in students, particularly amongst men. Most students report coping well, but additional supports are needed for an emotionally overwhelmed minority who report being unable to access help.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".