High tobacco use prevalence with significant regional and sex differences in smokeless tobacco use among Western Alaska Native people: the WATCH study
Bibliographic record
Abstract
Tobacco use prevalence among Alaska Native (AN) people living in Alaska is greater than the general population prevalence statewide and nationally. Better understanding of regional tobacco use is needed to improve cessation efforts and reduce prevalence. Using self-reported baseline data from the Western Alaska Tribal Collaborative for Health study, we describe tobacco use patterns among AN people in two western Alaska regions. Data were stratified by age group and sex. Dual- and multi-product use in the Yukon-Kuskokwim (Y-K) region was stratified by concurrent vs sequential use. Overall, 87% of the cohort reported having used tobacco. In Norton Sound, cigarette (98%) was the predominant tobacco type. In Y-K 71% smoked, 76% used smokeless tobacco (ST), with 47% reporting use of both products. ST use in Y-K consisted of commercial ST and homemade iqmik. Y-K women reported more ST product use, while men reported more cigarette use. Among dual- and multi-product users, the majority reported concurrent use, with no significant differences between men and women. Distinct regional differences include high smoking prevalence in Norton Sound and frequent use of smoking and ST products in Y-K. Findings support modification of cessation programmes to address regional variations in tobacco use patterns.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".