The association between public health engagement in school-based substance use prevention programs and student alcohol, cannabis, e-cigarette and cigarette use
Bibliographic record
Abstract
OBJECTIVE: This study examined the associations between public health engagement (PHE) in school-based substance use prevention programs and student substance use. For the purposes of this study, PHE refers to any form of collaboration between the local government public health agency and the school to promote the physical and mental health of students. METHODS: Data for this study were collected from the Cannabis, Obesity, Mental health, Physical activity, Alcohol use, Smoking and Sedentary behaviour (COMPASS) study during the 2018/2019 data collection year. Multilevel logistic regression was used to analyze the associations between PHE and student substance use. RESULTS: Data from 84 schools and 42,149 students were included; 70% of schools had PHE in substance use prevention programs. PHE in substance use prevention appears to have had no significant impact on student substance use in our models. When PHE was divided into five methods of engagement, it was found that when public health solved problems jointly with schools, the odds of a student using alcohol or cannabis significantly increased. When schools were split into low- and high-use schools for each substance measured, some methods of PHE significantly decreased the odds of cannabis and cigarette use in high-use schools and significantly increased the odds of alcohol and cannabis use in low-use schools. CONCLUSION: This study highlights the need to develop better partnerships and collaborations between public health and schools, and the importance of ensuring that school-based substance use prevention programs are evidence-based and tailored to the specific needs of schools and students.
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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 | 0.003 | 0.013 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".