Psychological distress among Canadian postsecondary students: a repeated cross-sectional analysis of the Canadian Campus Wellbeing Survey (CCWS) between spring 2020–2023
Bibliographic record
Abstract
Objective: To examine trends in self-reported psychological distress among Canadian postsecondary students between 2020 and 2023. Participants and Methods: Using data collected from postsecondary students (n = 103,936) through the Canadian Campus Wellbeing Survey (CCWS), multilevel regression models were fitted to determine how distress levels, as measured by the Kessler Psychological Distress Scale, differed across six-time points of the CCWS. Results: Across the cycles, students reported high levels of distress (mean across cycles = 26.16, SD = 8.61). Considering the impact of time on distress, when compared to pre-COVID-19 pandemic, Fall 2020 (β = 1.4, p < .001), Spring 2021 (β = 1.2, p < .001), Spring 2022 (β = 1.6, p < .001), and Spring 2023 (β = 0.80, p < .017) had significantly higher levels of distress. Distress levels were associated with ancestry, age, gender, and sexual orientation. Conclusion: It is imperative to develop strategies and allocate resources to address the high levels of psychological distress among Canadian postsecondary students.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".