Identifying factors associated with quit intentions among smokers from two nationally representative samples in Africa: Findings from the ITC Kenya and Zambia Surveys
Bibliographic record
Abstract
It is well established that intentions to quit smoking is the strongest predictor of future quit attempts. However, most studies on quit intentions have been conducted in high-income countries with very few in low- and middle-income countries particularly in Africa. This is the first population-based study to compare factors associated with quit intentions among smokers in two African countries. Data were from the International Tobacco Control (ITC) Kenya and Zambia Surveys (2012), face-to-face surveys of nationally representative samples of 2291 adult smokers (Kenya = 1103; Zambia = 1188). Multivariate logistic regression analyses were conducted to identify predictors of quit intentions. Most Kenyan (65.1%) and Zambian (69.1%) smokers had quit intentions of which 54.8% planned to quit within the next 6 months. Five factors were significantly associated with quit intentions in both countries: being younger, having tried to quit previously, perceiving that quitting is beneficial to health, worrying about future health consequences of smoking, and being low in nicotine dependence. The predictive strength of these factors did not differ in the two countries. Four additional factors were significant predictors in Zambia only: having a quit attempt lasting six months or more, lower smoking enjoyment, having a negative opinion about smoking, and concern about cigarette expenses. The factors predicting quit intentions were similar to those in other ITC countries including Canada, US, UK, China and Mauritius. These findings highlight the need for stronger tobacco control policies in Kenya and Zambia including increased taxation, greater access to cessation services, and anti-smoking campaigns denormalizing tobacco use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".