Comparing Self-Reported Symptoms of Anxiety and Depression Among Canadian Post-Secondary Students to the General Canadian Population During the COVID-19 Pandemic: A National Repeated Cross-Sectional Trend Analysis
Bibliographic record
Abstract
Over the past decade, and in particular since the onset of the COVID-19 pandemic, increasing proportions of Canadian post-secondary students have reported experiencing symptoms of mental illnesses and psychological distress, including anxiety and depression. The present study aims to describe trends in self-reported anxiety and depression reported by students during the COVID-19 pandemic period of February 2021 to January/February 2023 and to assess whether there were significant differences in these outcomes between students and the general adult population. A secondary analysis of a repeated measures cross-sectional dataset collected by Mental Health Research Canada (MHRC) was conducted to assess self-reported symptoms of anxiety and depression using the GAD-7 and PHQ-9 scales. Generalized linear models were used to assess how the prevalence of mental health symptoms changed over time, stratified by students compared to the general population. Generally, the proportion of respondents who screened positive for anxiety or depression was higher among the student compared to the general population across time points. Female respondents also had higher average scores on both scales compared to male counterparts. Proportions screening positive for both anxiety and depression were not significantly different over time points. Findings suggest Canadian post-secondary students experience higher levels of anxiety and depression compared to the general population, with females and students scoring particularly high on mental health assessments. It highlights the need for further research on the ongoing impact of the COVID-19 pandemic on student mental health and the importance of continuing mental health assessments through future MHRC polling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".