Canadian student and presenter perceptions of the appeal, appropriateness, and comprehensiveness of the CATCH My Breath vaping prevention curriculum
Bibliographic record
Abstract
Nicotine vaping is common among Canadian youth. However, there is a lack of evidence for effective school-based prevention approaches targeting nicotine vaping, particularly for high school students. This study explored Canadian student and presenter perceptions of the CATCH My Breath (CMB) curriculum's appeal, appropriateness, and comprehensiveness. A convenience sample of 10 high schools across Ontario, Canada, implemented the CMB curriculum between October 2022 and April 2023. A group of 40 students between 13 and 15 years old participated in six focus groups and 12 curriculum presenters [i.e. teachers and public health unit staff (PHU)] completed interviews to provide feedback about the appeal, appropriateness, and comprehensiveness of the curriculum for Canadian high school students. Qualitative thematic analysis identified major themes from both groups. Presenters and students highlighted key aspects of the curriculum, including the negative health risks of vaping, refusal skills development, and use of engaging activities. Presenters and students offered suggestions for improvement, including extending the session length, using up-to-date relevant statistics, and adding content (e.g. personal testimonies). Presenters and students generally thought that the curriculum was comprehensive, appealing, and appropriate for Canadian high school students. Future studies should evaluate the impacts of the curriculum on student vaping behaviours.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 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".