Alcohol prevention: What can be expected of a harm reduction focused school drug education programme?
Bibliographic record
Abstract
Aim: This pilot study investigated what alcohol prevention benefits could be achieved by a harm reduction focused school drug education intervention that addressed all drug use, both licit and illicit.Method: The study population comprised a cohort of 225 students in three intervention secondary schools and 93 students in a matched control school in Victoria, Australia. A classroom drug education programme, derived from evidence of effective practice and designed to reduce alcohol and other drug harm, was provided to the intervention students during years eight (13–14 year olds) and nine (14–15 year olds) by teachers trained in its delivery. The control students received the drug education programme normally provided by their school.Findings: The students, who received the intervention, were more knowledgeable about drug use issues, communicated more with their parents about alcohol, drank less, got drunk less, and experienced fewer alcohol related harms. They also remembered receiving more alcohol lessons. They were, however, no less likely to have tried alcohol.Conclusions: The findings are consistent with other studies that have demonstrated school alcohol education that focuses on harm reduction can be effective in reducing consumption, risk and harm. In this study, this was achieved even though the students were not persuaded against taking up drinking, and the intervention did not focus solely on alcohol. These findings have implications for both the goals and coverage of future school drug education programmes.
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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 | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".