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Record W4390695668 · doi:10.1177/10105395231220465

Intentions to Quit, Quit Attempts, and the Use of Cessation Aids Among Malaysian Adult Smokers: Findings From the 2020 International Tobacco Control (ITC) Malaysia Survey

2024· article· en· W4390695668 on OpenAlexaff
Ina Sharyn Kamaludin, Sin How Lim, Anne Yee, Susan Kaai, Mi Yan, Mahmoud Danaee, Amer Siddiq Amer Nordin, Farizah Mohd Hairi, Nur Amani Ahmad Tajuddin, Siti Idayu Hasan, Anne C K Quah, Geoffrey T. Fong

Bibliographic record

VenueAsia Pacific Journal of Public Health · 2024
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsOntario Institute for Cancer ResearchUniversity of Waterloo
Fundersnot available
KeywordsQuitlineSmoking cessationMedicineTobacco controlQuit smokingNicotine replacement therapyLogistic regressionMalayDemographyOddsOdds ratioFamily medicineEnvironmental healthPublic healthInternal medicineNursing

Abstract

fetched live from OpenAlex

This study examined quitting behavior and use of cessation aids (CAs) among Malaysian adult smokers aged ≥18 years (n = 1,047). Data were from the 2020 International Tobacco Control (ITC) Malaysia Survey were analyzed. A total of 79.9% of Malaysian smokers attempted to quit in the past 12 months and 85.2% intended to quit in the next 6 months. The most common CAs were e-cigarettes (ECs) (61.4%), medication/nicotine replacement therapies (NRTs; 51.0%), and printed materials (36.7%); the least common CA was infoline/quitline services (8.1%). Multivariable logistic regression analysis was performed to examine the association between sociodemographic variables and CAs use. Male smokers were more likely to use infoline/quitline services (adjusted odds ratio [aOR] = 3.27; P = .034). Malay smokers were more likely to use infoline/quitline services (aOR = 3.36; P = .002), ECs (aOR = 1.90; P = .004), printed materials (aOR = 1.79; P = .009), and in-person services (aOR = 1.75; P = .043). Most Malaysian smokers wanted to quit smoking. Furthermore, ECs were the most popular CAs, highlighting the need to assess the effectiveness of ECs for quitting smoking in Malaysia.

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)0.006
metaresearch head score (Gemma)0.002
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 score0.087
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.071
GPT teacher head0.321
Teacher spread0.250 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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