Smoking-attributable mortality among British Columbia’s first nations populations
Bibliographic record
Abstract
OBJECTIVES: First Nations (FN) people have high smoking rates and there is a need to examine their mortality related to smoking. METHODS: Smoking-attributable fractions and smoking-attributable mortality (SAM) rates were calculated for the FN and British Columbia (BC) populations during 1997 and 2001. RESULTS: Among FN adults, total age- and gender-adjusted SAM rates were 39.9 and 28.6 per 10,000 during 1997 and 2001, with potentially 19.0% and 17.3% of all deaths being preventable if smoking were eliminated. Among the BC adult population, total SAM age- and gender-adjusted rates were 27.8 and 25.3 per 10,000 during 1997 and 2001, and up to 21.8% and 20.8% of deaths were potentially preventable if smoking were eliminated. Among FN infants, SAM crude rates were 6.8 and 3.6 per 10,000 during 1997 and 2001, with 8.0% and 8.3% of infant deaths being potentially preventable if smoking were eliminated. Infant SAM crude rates among the general population were 1.4 per and 1.0 per 10,000 during 1997 and 2001 and 2.8% and 2.3% of deaths were potentially preventable if smoking were eliminated. CONCLUSIONS: Total adult age- and gender-adjusted SAM rates for both populations were substantive. Additional interventions that prevent and reduce tobacco use by FN people are indicated, particularly given their high rates of smoking. The high total SAM rates for FN infants also suggest the need for interventions.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".