Investigating Factors Associated with Adolescent Vaping and Cessation: A Qualitative Study
Bibliographic record
Abstract
Background: Since 2015, electronic-cigarette (e-cigarette) use increased among adolescents, necessitating research on effective cessation strategies. This qualitative research examined adolescents’ vaping behaviour and the perceived effectiveness of existing and proposed cessation resources, particularly Health Canada’s 2019 “Consider the Consequences of Vaping” video. Methods: In 2019, we conducted semi-structured interviews with 14 youth (ages 14-18) with e-cigarette use experience in Calgary, Alberta, Canada. The interviews were analyzed using qualitative description in NVivo. Results: Participants were introduced to vaping through members of their social groups. Parental guardians and friend groups were highly influential in encouraging and discouraging vaping. Almost all participants held the belief that vaping is healthier than smoking and said that Health Canada’s 2019 “Consider the Consequences of Vaping” campaign video required improvement. Many participants desired to cease vaping but lacked the resources to do so. Conclusions: This research suggests that a presentation from an ex-vaper, in addition to counselling might be the most useful resources to aid young people to cease vaping.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".