The Readiness of Smokers to Quit Smoking
Bibliographic record
Abstract
INTRODUCTION: The aim of the study is to find out the archives of smokers' readiness to quit smoking after the application of the Smoke-free House (RBAR) program. MATERIAL & METHODS: This type of research is a descriptive analytic study by using cross sectional approach. The research was conducted in a hamlet neighborhood (RW) that had been implementing RBAR program, and were selected randomly. They are RW 8 Tegal Panggung, RW 5 Tegal Panggung, RW 11 Ngupasan, RW 12 Bumijeo, RW 11 Gowongan. The samples were taken by cluster random sampling technique and obtained 70 heads of families with criteria of willing to be respondents, family heads (male), active smokers and permanent residents who lives in the study site since the RBAR program was first set in 2010. Data analysis was done by chi-square test. RESULTS: The result shows that attitudes has significant relation to the readiness of smokers to quit smoking after the application of the RBAR program (p value = 0.030). DISCUSSION & CONCLUSIONS: The carried out interventions can be adjusted to the stages of the smokers’ behavior change process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".