Factors Associated with Quit Intentions among Adult Smokers in South Korea: Findings from the 2020 ITC Korea Survey
Bibliographic record
Abstract
Background: South Korea has made substantial progress on tobacco control, but cigarette smoking prevalence is still high. Previous studies were conducted before the use of nicotine vaping products (NVPs) or heated tobacco products (HTPs) became popular. Thus, whether the concurrent use of NVPs or HTPs affects quit intentions among Korean smokers remains a question that needs to be explored. This study aims to identify predictors of quit intentions among cigarette-only smokers and concurrent users of cigarettes and NVPs or HTPs. Methods: Data were from the 2020 International Tobacco Control Korea Survey. Included in the analysis were 3778 adult cigarette smokers: 1900 at-least-weekly exclusive smokers and 1878 at-least-weekly concurrent smokers and HTP or NVP users. Bivariate and multivariable logistic regression analyses were conducted. Results: Quit intentions were reported by 66.4% of respondents. Factors significantly associated with quit intentions included younger age, having a spouse/partner, lower nicotine dependence, reporting a past quit attempt, regretting starting smoking, believing that smoking had damaged health, worrying that smoking will damage future health, and perceiving health benefits of quitting. Current use of NVPs or HTPs was not significantly associated with quit intentions. Conclusions: This study contributes the following to current literature: intrinsic health-related beliefs were more important than societal norms in shaping quit intentions. These findings should be considered in shaping future smoking cessation policies, such as reinforcing education programs that emphasize the benefits of quitting for personal health reasons, lowering nicotine dependence, and encouraging multiple quit attempts and successful quitting.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".