Prevalence of cigarette smoking among young adults in Pakistan.
Bibliographic record
Abstract
OBJECTIVE: To obtain information about the prevalence of cigarette smoking among a selected sample of university students in Karachi and build our understanding of the determinants of smoking with respect to family smoking, smoking in the home, smoke-free public places, and quit smoking cessation programmes. METHODS: Data were collected as a part of a pilot project initiated by Jinnah University Karachi. Participants were 629 university students (432 males and 197 females) aged 18-25 years from ten universities in Karachi. Descriptive statistics and Logistic regression analyses were used to determine the results and conclusions. RESULTS: Thirty-nine per cent of students had smoked a whole cigarette in their life time, whereas 25% had smoked 100 or more cigarettes in their lifetime. Overall, 23% of students (31% male and 6% female) were classified as a current smoker and their mean age and standard deviation of smoking initiation was 17 +/- 2.7 years (17 +/- 2.6) for males and 16 +/- 2.9 females. Sixty-three percent of smokers reported that public places should be smoke-free. Logistic regression analyses adjusted by age and gender suggested that parental and sibling influence and number of close friends and individuals who smoke at home were highly predictive of being a smoker. CONCLUSION: Findings from this study suggest that student were generally open to smoking cessation treatment and no-smoking restrictions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".