Tobacco cessation pharmacotherapy use among First Nations persons residing within British Columbia
Bibliographic record
Abstract
The rate of tobacco use is higher among First Nations (FN) peoples than among other Canadians. Cessation pharmacotherapy agents reduce tobacco use, but the appropriateness and effectiveness of these agents among FN smokers are not entirely clear. Rates of tobacco cessation pharmacotherapy use among FN smokers are unknown; such information would be useful for program planners and would indicate appropriateness of use. To examine cessation pharmacotherapy use, we extracted claims for nicotine gum, nicotine patch, and bupropion SR (Zyban) from the Non-Insured Health Benefits pharmacy database for FN persons living within British Columbia during 2001. A total of 3.8% (95% CI=3.6-4.0) of FN claimants filled a prescription for at least one tobacco cessation pharmacotherapy agent; 61.7% were female, and their mean age was 38.1 years. Most claims (60.5%) were for bupropion, followed by nicotine patch (40.7%) and nicotine gum (4.7%). A total of 4.6% of claimants used both nicotine patch and bupropion,.8% used nicotine gum and nicotine patch, and.5% used nicotine gum and bupropion. Pharmacotherapy agents appear to be used less often by FN smokers than by other Canadian smokers for several possible reasons. Additional research is needed related to FN populations and cessation pharmacotherapy use in terms of cultural appropriateness, barriers to use, and effectiveness.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".