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Record W2104636178 · doi:10.1080/14622200802027172

Prevalence and correlates of roll-your-own smoking in Thailand and Malaysia: Findings of the ITC-South East Asia Survey

2008· article· en· W2104636178 on OpenAlexaff
David Young, Hua‐Hie Yong, Ron Borland, Hana Ross, Buppha Sirirassamee, Foong Kin, David Hammond, Richard J. O’Connor, Geoffrey T. Fong

Bibliographic record

VenueNicotine & Tobacco Research · 2008
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversity of Waterloo
FundersNational Cancer Institute
KeywordsEnvironmental healthTobacco controlMedicineTobacco useTobacco productTraditional medicineDemographyPublic healthPopulationSociology

Abstract

fetched live from OpenAlex

Roll-your-own (RYO) cigarette use has been subject to relatively limited research, particularly in developing countries. This paper seeks to describe RYO use in Thailand and Malaysia and relate RYO use to smokers' knowledge of the harmfulness of tobacco. Data come from face-to-face surveys with 4,004 adult smokers from Malaysia (N = 2,004) and Thailand (N = 2000), collected between January and March 2005. The prevalence of any use of RYO cigarettes varied greatly between Malaysia (17%) and Thailand (58%). In both countries, any RYO use was associated with living in rural areas, older average age, lower level of education, male gender, not being in paid work, slightly lower consumption of cigarettes, higher social acceptability of smoking, and positive attitudes toward tobacco regulation. Among RYO users, exclusive use of RYO cigarettes (compared with mixed use) was associated with older age, female gender (relatively), thinking about the enjoyment of smoking, and not making a special effort to buy cheaper cigarettes if the price goes up. Finally, exclusive RYO smokers were less aware of health warnings (RYO tobacco carries no health warnings), but even so, knowledge of the health effects of tobacco was equivalent.

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)0.002
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 score0.003
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.102
GPT teacher head0.347
Teacher spread0.245 · 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

Citations28
Published2008
Admission routes1
Has abstractyes

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