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Record W1985252479 · doi:10.3402/ijch.v63i1.17651

Smoking-attributable mortality among British Columbia’s first nations populations

2004· article· en· W1985252479 on OpenAlexaffabout
Dennis Wardman, Nadia Khan

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2004
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsFirst Nations Health and Social Secretariat of ManitobaUniversity of British ColumbiaHealth Canada
Fundersnot available
KeywordsDemographyGeographyEnvironmental healthMedicineGerontologySociology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.356
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2004
Admission routes2
Has abstractyes

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