Factors Associated with Attempt for Smoking Cessation among Hardcore Smokers in Taiwan
Bibliographic record
Abstract
Background: Tobacco control activities have mostly influenced those smokers who found it easier to quit and, thus, remaining smokers are those who are less likely to stop smoking. This phenomenon is called “hardening hypothesis,” which individuals unwilling or unable to quit smoking and likely to remain so. The aim of this study was to identify the factors correlated with smoking cessation among hardcore smokers. Methods: A cross-sectional descriptive correlational research design was employed. Hardcore smokers from communities in Taiwan were recruited to participate in the study (N = 187). Self-report questionnaires were used to collect demographic data as well as data on nicotine dependence, quitting self-efficacy, social smoking motives, attitudes towards the Tobacco Hazards Prevention Act (THPA), and smoking cessation. Logistic regression analysis was used to examine the factors that were related to quit smoking. Results: About 30.3% (n = 54) reported having experienced quitting smoking over 7 days in the past year. Logistic regression analysis indicated that attitudes towards the THPA was identified as a particularly important factor contributing to the increase in smoking cessation among hardcore smokers. Conclusions: Nurses should cooperate with smoking cessation coaches to facilitate the improvement of attitudes towards the THPA as a key means through which to increase the smoking cessation rate among hardcore smokers.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".