Predictors of quit intentions among adult smokers in Mauritius: Findings from the ITC Mauritius Survey
Bibliographic record
Abstract
INTRODUCTIONMauritius has one of the highest rates of smoking in Africa.Smoking cessation is a priority for preventing tobacco-related morbidity and mortality.The purpose of this study is to identify the predictors of quit intentions among smokers in Mauritius in order to strengthen tobacco control policies and inform the development and delivery of services that may increase the likelihood of successful quitting.METHODS Data were drawn from Wave 1 (2009) of the International Tobacco Control (ITC) Mauritius Survey, a face-to-face cohort survey of a nationally representative sample of 598 adult smokers who were randomly selected from nine geographic districts in Mauritius using a multistage sampling procedure. RESULTSThe vast majority of smokers (77.8%) had plans to quit smoking.Longer duration of past quit attempts (6 months or less), perceiving benefits of quitting, worrying about smoking damaging health in the future, and not enjoying smoking were significantly associated with quit intentions.However, socio-demographic characteristics, past quit attempts, overall attitude about smoking, and Heaviness of Smoking Index (HSI) were not associated with quit intentions.CONCLUSIONS The predictors of quit intentions among Mauritian smokers were generally similar to those found among smokers in other high-and middle-income countries.However, in contrast to findings in those other countries, nicotine dependence as measured by the HSI was not a significant predictor of quit intentions among Mauritian smokers.These findings highlight the need to consider the predictors of quit intentions when developing and delivering smoking cessation support services in Mauritius. INTRODUCTIONTobacco use continues to be the leading cause of preventable morbidity and premature death in the world.It is estimated that each year, tobacco use kills six million people (out of the one billion smokers), of which about 80% are from lowand middle-income countries 1 .Smoking cessation among adult smokers is critically important to improving public health initiatives because about 50% of smokers die from tobacco-related diseases 1 .Moreover, promoting cessation among smokers provides significant short-term and mediumterm benefits to reducing smoking-attributable diseases and mortality relative to prevention among non-smoking youth 1 .However, smoking is a complex behaviour that is difficult to Predictors of quit intentions among adult smokers in Mauritius: Findings from the ITC Mauritius Survey
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".