Assessment of 2016 Taiwan tobacco control performance based on WHO MPOWER guidelines
Bibliographic record
Abstract
Background Smoking kills more than 27,000 people each year in Taiwan. This study aims to demonstrate the significant progress with Taiwan MPOWER performances and compare achievements with OECD and other Asian countries. Methods Assessment data from Taiwan's laws and regulations, a comprehensive health surveillance system, and research database and reports, with WHO MPOWER performance rating indicators. Results The adult smoking rate declined from 21.9% in 2008 to 15.3% in 2016, indicating a 30.3% decline in smoking rate and a decrease in the number of smokers by approximately 810,000 within 8 years. Taiwan has protected over 90% of the population from smoke-free environments by enforcing the law in eliminating second hand smoke in public areas, and promoted the recognition of tobacco hazards to youth especially via campus activities, official websites, Facebook, and YouTube. It´s worth noting that Taiwan is the 2 nd country to carry out the ordinance of smoke-free parks and national parks around the world. Assisting over 110,000 smokers since 2012 in quitting smoking successfully by providing the Second Generation Smoking Cessation Program and other services such as Quitline, cessation classes and cessation counseling. Over 40% of smokers attempt to quit after Taiwan enacted health warning labels on tobacco packs. The Health Promotion Administration has worked with the National Communications Commission (NCC) to establish the guideline that anti-tobacco textual messages are required to add on Radio and TV programs, reminding people of tobacco harming. Conclusions Taiwan gains five 1 st performance (Monitoring, Smoke-free environments, Cessation programmes, Anti-tobacco mass media campaigns, Bans on advertising, promotion and sponsorship) while compared to OECD and other Asian countries (New Zealand, Japan, Republic of Korea, Thailand, Taiwan China, etc.), and ranks among the best with Singapore, Canada, United Kingdom, and Australia. Continued efforts are needed to raise the health warnings and taxations in the future.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".