Public health unit engagement in school mental health programs and adolescent mental health during the COVID-19 pandemic: COMPASS, 2018–2022
Bibliographic record
Abstract
BACKGROUND: Public health unit (PHU) engagement in schools is important for promoting wellness in students. We aimed to investigate if PHU engagement with schools may have provided protection against the risk of depression and anxiety in students during the COVID-19 pandemic. METHODS: We used longitudinal data from the Cannabis, Obesity, Mental health, Physical activity, Alcohol use, Smoking and Sedentary behaviour survey between the 2018/19 and 2020/21 academic years. Multilevel models were used to assess the association between PHU engagement with school mental health programs prior to the COVID-19 pandemic and depressive (Center for Epidemiologic Studies Depression scale Revised) and anxiety symptoms (Generalized Anxiety Disorder scale) during the COVID-19 pandemic. RESULTS: The sample included 23 894 students across 104 secondary schools in British Columbia, Alberta, Ontario and Quebec. In confounder-adjusted models, PHU engagement before the pandemic was not associated with student depressive symptoms (B = -0.01, 95% CI = -0.04, 0.02), but was protective against anxiety symptoms (B = -0.03, -0.06, 0.001) during the COVID-19 pandemic. DISCUSSION: The results highlight that PHU engagement with mental health programming in schools was protective against anxiety for students during the COVID-19 pandemic. The findings support the importance of PHU engagement for improving student mental health and pandemic recovery.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".