What Motivates Smoking Cessation? A Cross-Sectional Study in a Lower-Middle-Income Country
Bibliographic record
Abstract
Abstract IntroductionOnly one-quarter of smokers in Pakistan attempt to quit smoking, and less than 3% are successful. In the absence of any literature from the country, this study aimed to explore factors motivating and strategies employed in successful smoking cessation attempts in Pakistan, a lower-middle-income country.MethodsA survey was carried out in Karachi, Pakistan, amongst adult (≥ 18 years) former smokers (individuals who had smoked ≥ 100 cigarettes in their lifetime but who had successfully quit smoking for > 1 month at the time of survey). Multivariable logistic regression, adjusted for age, gender and monthly family income, was performed with number of quit attempts (single vs. multiple) as the dependent variable.ResultsOut of 330 former smokers, 50.3% quit successfully on their first attempt (50.3%) with 62.1% quitting “cold turkey”. Only 10.9% used a cessation aid (most commonly nicotine replacement therapy; 8.2%). Motivations for quitting included self-health (74.5%), promptings by one’s family (43%), and family’s health (14.8%). Other social pressures included peer-pressure to quit smoking (31.2%) and social avoidance by non-smokers (22.7%). Successful smoking cessation on one’s first attempt was associated with being married (OR: 3.239 [95% CI:1.593-6.585]), employing an abrupt cessation mode of quitting (4.144 [2.512-6.836]), and telling oneself that one has the willpower to quit (1.635 [1.027-2.604]).ConclusionIn Pakistan, smoking cessation is motivated by concern for self-health and family’s health, family’s support, and social pressures. Our results lay a comprehensive foundation for the development of smoking-cessation interventions tailored to the population of the country.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".