The Impact of the COVID-19 Pandemic on the Mental Health of First-Year Undergraduate Students Studying at a Major Canadian University: A Successive Cohort Study
Bibliographic record
Abstract
ObjectiveTo examine the impact of the COVID-19 pandemic on first year undergraduate student mental health.MethodsAs part of the Queen’s University U-Flourish Student Well-Being and Academic Success study, three successive cohorts of students entering undergraduate studies in 2018 (pre-pandemic), 2019 (transitional), and 2020 (during pandemic) completed electronic surveys at entry and completion of first year. Validated self-report measures were used to assess mental health status including symptom levels of anxiety, depression, and insomnia, self-harm and frequency of substance use. Propensity matching and multivariable log-binomial regression were used in comparisons of mental health indicators across the cohorts.ResultsClinically significant symptoms of depression, anxiety, insomnia, and self-harm were reported more frequently in the 2020–2021 cohort, coincident with remote learning and pandemic restrictions. In female students, screen positive rates for anxiety and depression, and suicidal ideation increased from about one-third to just under one-half in association with the pandemic (χ2, p ConclusionsMental health concerns including anxiety, depression and sleep problems increased in first year students during the pandemic, especially among females, while alcohol use declined. These findings highlight the negative mental health impact associated with studying under pandemic restrictions involving remote learning and social distancing.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".