Cross-sectional trend analysis of the NCHA II survey data on Canadian post-secondary student mental health and wellbeing from 2013 to 2019
Bibliographic record
Abstract
BACKGROUND: Canadian post-secondary students are considered to be at risk for chronic stress and languishing mental health, but there has been no longitudinal analysis of the available population-level data. The purpose of this study was to examine trends in the overall and sex-specific prevalence of self-reported stress, distress, mental illness, and help seeking behaviours among Canadian post-secondary students over the past several years. METHODS: Using the 2013, 2016, and 2019 iterations of the National College Health Assessment II Canadian Reference data, we conducted a trend analysis for each variable of interest, stratified by sex. The significance and magnitude of the changes were modelled using cumulative linked ordinal regression models and log binomial regression models. RESULTS: With few exceptions, we observed significant increases over time in the proportion of students reporting symptoms of psychological distress, mental illness diagnoses, and help seeking for mental health related challenges. Female students reported a higher level of stress than male students, with a statistically significant increase in the stress level reported by female students observed over time. In all cases, larger proportions of female students were observed compared to male students, with the proportion of female students who self-reported mental illness diagnoses nearly doubling that of males. CONCLUSIONS: Our analysis indicated that the proportion of students self-reporting mental health related challenges, including stress, psychological distress, and diagnosed mental illnesses increased between the 2013, 2016 and 2019 iterations of the NCHA II conducted among Canadian post-secondary 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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".