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Record W2103573991 · doi:10.1093/heapro/dag021

Tobacco control and gender in Southeast Asia. Part I: Malaysia and the Philippines

2003· article· en· W2103573991 on OpenAlexfundno aff
Martha G. Morrow, Simon Barraclough

Bibliographic record

VenueHealth Promotion International · 2003
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersAustralian Research CouncilUniversity of ReginaWorld Bank Group
KeywordsTobacco controlPromotion (chess)Political scienceContradictionEnvironmental healthHealth promotionEconomic growthDevelopment economicsGender studiesMedicineSociologyPublic healthHealth carePolitics

Abstract

fetched live from OpenAlex

In the World Health Organization's Western Pacific Region, being born male is the single greatest risk marker for tobacco use. While the literature demonstrates that risks associated with tobacco use may vary according to sex, gender refers to the socially determined roles and responsibilities of men and women, who initiate, continue and quit using tobacco for complex and often different reasons. Cigarette advertising frequently appeals to gender roles. Yet tobacco control policy tends to be gender-blind. Using a broad gender-sensitivity framework, this contradiction is explored in four Western Pacific countries. Part I of the study discusses issues surrounding gender and tobacco, and analyses developments in Malaysia and the Philippines. Part II deals with Singapore and Vietnam. In all four countries, gender was salient for the initiation and maintenance of smoking, and in Malaysia and the Philippines was highly significant in cigarette promotion. Yet, with a few exceptions, gender was largely unrecognized in control policy. Suggestions for overcoming this weakness in order to enhance tobacco control are made in Part II.

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)0.000
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 score0.057
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.053
GPT teacher head0.343
Teacher spread0.289 · 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

Citations75
Published2003
Admission routes1
Has abstractyes

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