Factors that influence the decision to vape among Indigenous youth
Bibliographic record
Abstract
The use of e-cigarettes (vaping) among Indigenous youth is much higher than that of their non-Indigenous counterparts, which has raised the concerns of various Indigenous scholars and communities. To better understand the most salient constructs that influence Indigenous youth decision-making around vaping, we co-created a qualitative research study with a Syilx First Nation community that was guided by the Unified Theory of Behavior (UTB). Methods Through semi-structured interviews and a sharing circle, we gathered the perspectives and experiences of 16 Syilx youth in British Columbia, Canada. After an initial collaborative coding and training session, the interviews were transcribed and coded by Indigenous peer researchers using Nvivo. Through both directed and conventional qualitative content analysis methods, the final conceptual framework was collaboratively developed. Results Syilx youth reported that vaping decision-making is underpinned by colonialism, and the historical disproportionate impact of the tobacco industry. The youth spoke to several individual determinants that influence intentions to vape (e.g., vaping helps you cope) and to not vape (e.g., family and community connectedness), and determinants that translate intentions to vape to decision to vape (e.g., access to vaping), and to not vape (e.g., access to trusted adults and support from the band). The youth suggested that prevention efforts must be informed by an understanding of why Indigenous youth vape and what strengthens their resolve to not vape. Conclusions Vaping decision-making among Indigenous youth is underpinned by their cultures, contexts, and histories. To effectively address vaping among Indigenous youth, continued engagement of Indigenous youth in planning, developing, implementing, and evaluating both prevention and policies efforts is a necessity.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".