Protective Factors Against e‐Cigarette Use Among First Nations People Aged 16–24 in the Next Generation Youth Wellbeing Study
Bibliographic record
Abstract
ISSUE ADDRESSED: Adolescent e-cigarette use is increasing and is associated with subsequent smoking. This study examines potential protective factors associated with not vaping among First Nations adolescents in Australia to inform community programs. METHODS: The 'Next Generation: Youth Wellbeing Study' is a cohort study of First Nations adolescents aged 10-24 years from urban, rural and remote communities in Central Australia, Western Australia and New South Wales. Analysis of self-reported vaping from 16 to 24-year-olds, collected 2018-2020, using multi-level mixed-effects Poisson regression to estimate age-site-adjusted prevalence ratios (PRs) for never-vaping in relation to various factors. RESULTS: Among 419 participants, 65% were female, 75% had never vaped, 49% had never smoked and 82% lived in smoke-free homes. Never vaping was more common among those who had: never-smoked (PR = 1.78, 95%CI: 1.56-2.04); never used cannabis (1.89, 1.60-2.24); non-smoking friends (1.38, 1.26-1.51); good mental health (1.15, 1.01-1.30), never diagnosed with depression (1.21, 1.01-1.46) or anxiety (1.31, 1.08-1.57); and no experiences of racism (1.21, 1.08-1.36), no negative criminal justice system experiences (1.25, 1.11-1.41), or vicarious racism through negative media (1.24, 1.10-1.39). CONCLUSIONS: Most First Nations adolescents have never vaped, with potential protective factors being better mental health, no other substance use and fewer experiences of racism and justice system interactions. Comprehensive community adolescent prevention programs are needed to prevent vaping and protect future health, including preventing nicotine addiction and future smoking. SO WHAT?: Policies and programs must address e-cigarettes directly as well as structural factors, promoting broader adolescent wellbeing, centring culture and family in a strengths-based approach.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".