Factors that influence decision-making among youth who vape and youth who don’t vape
Bibliographic record
Abstract
Vaping rates among Canadian youth are significantly higher compared to adults. While it is acknowledged that various personal and socio-environmental factors influence the risk of school-aged youth for vaping uptake, we don't know which known behavior change factors are most influential, for whom, and how. The Unified Theory of Behavior (UTB) brings together theoretically-based behavior change factors that influence health risk decision making. We aimed to use this framework to study the factors that influence decision making around vaping among school-aged youth. Qualitative interviews were conducted with 25 youth aged 12 to 18 who were either vaped or didn't vape. We employed a collaborative and directed content analysis approach and the UTB constructs served as the coding framework for analysis. Gender differences were explored in the analysis. We found that multiple intersecting factors play a significant role in youth decision making to vape. Youth who vaped and those who did not vape reported similar mediating determinants that either reinforced or challenged their decision-making, such as easy access to vaping, constant exposure to vaping, and the temptation of flavors. Youth who didn't vape reported individual determinants that strengthened their intentions to not vape, including more negative behavioral beliefs (e.g., vaping is harmful) and normative beliefs (e.g., family disapproves), and strong self-efficacy (e.g. self-confidence). Youth who did vape, however, reported individual determinants that supported their intentions to vape, such as social identity, coolness, and peer endorsement. The findings revealed cohesion across multiple determinants, suggesting that consideration of multiple determinents when developing prevention messages would be beneficial for reaching youth.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".