Shifting school health priorities pre–post cannabis legalization in Canada: Ontario secondary school rankings of student substance use as a health-related issue
Bibliographic record
Abstract
This study examined how schools prioritize ten key health concerns among their student populations over time and whether schools' prioritization of alcohol and other drug use (AODU) corresponds to students' substance use behaviours and cannabis legalization as a major policy change. Data were collected from a sample of secondary schools in Ontario, Canada across four years (2015/16-2018/19 [N2015/16 = 65, N2016/17 = 68, N2017/18 = 61 and N2018/19 = 60]) as a part of the COMPASS study. School-level prevalence of cannabis and alcohol use between schools that did and did not prioritize student AODU as a health concern was examined. Ordinal mixed models examined whether student cannabis and alcohol use were associated with school prioritization of AODU. Chi-square tests examined changing health priorities among schools pre-post cannabis legalization. School priority ranking for AODU was mostly stable over time. While AODU was identified as an important health concern, most schools identified mental health as their first priority across the four years of the study. No significant changes to school AODU priorities were observed pre-post cannabis legalization nor was school prioritization of AODU associated with student cannabis and alcohol use behaviours. This study suggests that schools may benefit from guidance in identifying and addressing priority health concerns among their student population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".