Smoking patterns and readiness to quit--a study of the Australian Arabic community.
Bibliographic record
Abstract
BACKGROUND: Smoking cessation interventions have typically focused on majority populations who, in Australia, are English speaking. There has been an overall decline in the prevalence of smoking in the Australian community. However, there remains a relative paucity of useful information about tobacco use and the effectiveness of tobacco interventions among specific ethnic minorities. OBJECTIVE: To determine associations of tobacco use and tobacco control indicators for Arabic speakers seen in the Australian general practice setting. METHODS: A cross sectional study in a consecutive sample of Arabic patients (n=1371) attending the practices of 29 Arabic speaking general practitioners in Sydney, New South Wales. RESULTS: Twenty-nine (53.7%) of 54 eligible Arabic speaking GPs in southwest Sydney participated in this study. Of 1371 patients seen, 29.7% were smokers. Smokers were more likely to report poorer health (chi2=21.7, df=1, p<0.001); 35.7% reported high nicotine dependence. Dependence was more in men (chi2=11.7, df=1, p<001) and those who reported poorer health (chi2=4.9, df=1, p<0.03); 35.9% had attempted to quit in the previous year; 17% were in preparation stage of change; 42.7% recalled quit advice. Poorer self reported health status (AOR=2.13, 95% CI: 1.14-3.97, p=0.017) and unemployment (AOR=1.69, 95% CI: 1.51-4.90, p=0.033) were independent predictors of advice from a health professional, most often a GP (71%). CONCLUSION: Our study confirms previous reports that the proportion of self reported current smokers among the Arabic community is higher than for the Anglo-European majority. There is a need for ethno specific campaigns in tobacco control.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".