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Record W3093493022 · doi:10.1177/1010539520965370

Tobacco Use and Cessation Among a National Online Sample of Men Who Have Sex With Men in Malaysia

2020· article· en· W3093493022 on OpenAlexaff
Sin How Lim, Lujain Daghar, Chris Bullen, Hanisah Muhammad Faiz, M. Bilal Akbar, Amer Siddiq Amer Nordin, Anne Yee

Bibliographic record

VenueAsia Pacific Journal of Public Health · 2020
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsMcGill University
FundersWorld Bank Group
KeywordsPsychosocialMen who have sex with menMedicineEthnic groupSmoking cessationDemographyPsychological interventionSexual minorityDemographicsHuman immunodeficiency virus (HIV)PsychiatryPsychologySexual orientationFamily medicineSocial psychologySyphilis

Abstract

fetched live from OpenAlex

Previous studies documented the health disparities in smoking among sexual minority populations, including men who have sex with men (MSM). However, smoking behaviors have never been examined among Malaysian MSM, a sexual minority group in a predominantly Muslim country. A total of 622 Malaysian MSM completed an anonymous online survey in 2017. Data on the demographics, smoking and substance use behaviors, psychosocial factors, and attitudes toward smoking cessation were collected and analyzed. The mean age was 28 years and 67% of participants were of Malay ethnicity. The prevalence of current smoking was 23% (n = 143), while former smokers were 9% (n = 59). Current smoking status was associated with HIV-positive status and risk behaviors, such as suicidality, alcohol use, and illicit drug use ( P = .001). Almost two thirds of current smokers had attempted to quit in the past year. Hence, comprehensive smoking cessation interventions addressing the psychosocial needs of MSM should be prioritized.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.017
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.073
GPT teacher head0.354
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 source (direct Gemma or distilled Codex), 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

Citations4
Published2020
Admission routes1
Has abstractyes

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