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Record W2094367026 · doi:10.1080/14622200410001734094

Tobacco cessation pharmacotherapy use among First Nations persons residing within British Columbia

2004· article· en· W2094367026 on OpenAlexaffabout
A. E. Dennis Wardman F.R.C.P.C, Nadia Khan

Bibliographic record

VenueNicotine & Tobacco Research · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicTraditional and Medicinal Uses of Annonaceae
Canadian institutionsUniversity of British ColumbiaFirst Nations Health and Social Secretariat of ManitobaHealth Canada
Fundersnot available
KeywordsLibrary scienceSmoking cessationSuiteMedicinePharmacotherapyFamily medicinePolitical sciencePsychiatryLaw

Abstract

fetched live from OpenAlex

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.

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 score0.216
Threshold uncertainty score0.982

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 studies0.0010.000
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 head0.048
GPT teacher head0.329
Teacher spread0.281 · 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

Citations6
Published2004
Admission routes2
Has abstractyes

Explore more

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