The Necessity of Pharmacies Participating in the National Smoking Cessation Service
Bibliographic record
Abstract
As of 2022, Korea's adult smoking rate stands at 17.7%, reflecting a decline in smoking.However, the overall tobacco use rate remains stagnant due to the increasing popularity of alternatives like heated tobacco products and electronic cigarettes (e-cigarettes).More smokers are turning to these products instead of quitting, and fewer are attempting to quit, underscoring the need for reform in the national smoking cessation services.Countries like Australia, Canada, and Thailand effectively involve pharmacies in smoking cessation efforts.In Australia and Canada, these services are integrated into primary healthcare, with pharmacists offering counseling supported by national guidelines and financial assistance for medications.Thailand provides specialized training for pharmacy students on the health effects of smoking and cessation support.In Canada, pharmacy-led interventions have a success rate of 36%, significantly higher than the 7% success rate for unaided quit attempts.Korea should actively involve pharmacies in these initiatives to enhance its national smoking cessation efforts.This would include developing comprehensive training programs for pharmacists, establishing pharmacy-based support services, and formally integrating pharmacies into the country's cessation support infrastructure.By implementing these strategies, Korea can strengthen its approach to smoking cessation, offering better support to individuals aiming to quit and ultimately fostering a healthier population.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.078 | 0.011 |
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".