The smoking population is not hardening in South Korea: a study using the Korea Community Health Survey from 2010 to 2018
Bibliographic record
Abstract
INTRODUCTION: The hardening hypothesis proposes that the proportion of hardcore smokers increases when smoking prevalence declines. To evaluate whether such hardening occurs in South Korea, we examined the association between quitting behaviours, the number of cigarettes smoked per day and the proportion of hardcore smokers and smoking prevalence among local districts in South Korea. METHODS: This study used the cross-sectional data from the Korea Community Health Survey (2010-2018) to examine local district-level associations between smoking prevalence and quit attempts, quit plans, quit ratios, cigarettes smoked per day and the proportion of hardcore smokers. Panel regression analysis was performed using the indicators of hardcore smoking (quit attempts, quit plans, quit ratios, cigarettes smoked per day and proportion of hardcore smokers) as the outcome variables, and prevalence of smoking, local districts, age and sex as predictor variables. RESULTS: When the smoking prevalence of the districts decreased by 1%, quit attempts, quit plans and quit ratios increased by 0.24% (95% CI 0.11 to 0.37), 0.37% (95% CI 0.26 to 0.47) and 1.71% (95% CI 1.65 to 1.76), respectively. Cigarette consumption decreased by 0.17 cigarettes per day (95% 0.15 to 0.19), and the prevalence of hardcore smokers decreased by 0.88% (95% CI 0.78 to 0.98) when smoking prevalence decreased by 1%. CONCLUSION: Hardening of smoking did not occur in South Korea when smoking prevalence declined, which suggests tobacco control policies in South Korea have been effective in reducing smoking prevalence without increasing the proportion of hardcore smokers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".