New Ways of Helping Poor Smokers to Quit in Central Java, Indonesia
Bibliographic record
Abstract
This report describes a pilot cessation \n study aimed to test well-proven approaches to helping \n smokers quit in a resource-poor setting. The \n group-randomized trial (by village) included 788 poor \n smokers in 18 villages. Participants were assigned to one of \n three intervention groups : counseling only, nicotine \n patches only, and a combination of both. 47 people dropped \n out soon after the interventions began. Quit rates varied \n across the intervention groups, and were significantly \n higher for the two groups that received counseling. Whether \n or not the counseling groups received nicotine patches made \n little difference to outcomes. The 12-month continuous \n abstinence rates were 17 percent for the counseling only \n group, 15 percent for the counseling plus NRT group, and 7 \n percent for the group that received nicotine patches only. \n The results suggest that cessation support programs could be \n successful and cost effective in Indonesia, and achieve \n comparable results to similar efforts in America, Canada, \n Australia, the UK and Europe.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".