From didactic to dialogue: Assessing the use of an innovative classroom resource to support decision-making about cannabis use
Bibliographic record
Abstract
Aims: In most countries, cannabis use rates are highest among young people. Efforts invested in cannabis prevention programmes have had limited success. In part, this may be attributed to a dearth of meaningful discussion in classroom settings on the topic and scarcity of credible resources. Although young people want opportunities to engage in dialogue focussed on cannabis, educators often feel unprepared to facilitate such discussions. Methods: In this knowledge translation study based on recent ethnographic findings, a film was created to explore decision-making and cannabis use among young people. Accompanying curricular materials were developed to support adult facilitators in leading group discussions. Findings: The film-based resource was used in 55 sites across Canada by 48 facilitators (school staff, public health professionals and youth workers); the film was viewed by more than 2500 students. Qualitative content analysis of facilitator evaluations along with telephone interviews revealed the impact of using the innovation. Facilitators adapted the resource in a variety of classes where in-depth discussions occurred, generating critical self-reflection. Conclusions: The diffusion of this drug education innovation underscores the importance of youth engagement in prevention programmes. Prevention approaches that accommodate inclusive and balanced discussion about cannabis use can support young people in their decision-making.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".