Educational differences in awareness and use of the outpatient smoking cessation services program in Taiwan
Bibliographic record
Abstract
Background: To reduce educational inequalities in smoking, it is important that smoking cessation services can reach lesseducated smokers. Studies on inequalities in awareness or use of smoking cessation services from Asian are lacking. This study assesses educational differences in awareness and use of a nationwide outpatient smoking cessation program in Taiwan.Methods: A total of 6,461 current smokers aged 25-64 was drawn from cross-sectional nationwide data of the 2012-2014 Adult Smoking Behaviour Survey. Multivariate logistic regression models were used to estimate the association between education and program awareness among current smokers and the association between education and program utilization among current smokers who were aware of the program and attempted to quit in the past 12 months.Results: About 27% of current smokers were aware of the program. The odds of being aware of the program were higher for high school graduates (AOR = 1.38, 95% CI = 1.02-1.86) and those with at least a college degree (AOR = 1.89, 95% CI = 1.33-2.70), compared to those with middle school or less education. About a quarter of those who were aware of the program and attempted to quit have used the program. There were no educational differences in program utilization.Conclusions: Given the educational inequalities in awareness of the outpatient program, tobacco-control policies should develop strategies to reach less educated smokers and overcome such inequalities.
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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 | 0.003 |
| 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".