www.mdpi.com/journal/ijerph Article Perception of Tourists Regarding the Smoke-Free Policy at
Bibliographic record
Abstract
Abstract: This study was conducted during February-March 2012 to determine the perception and support regarding smoke-free policy among tourists at Suvarnabhumi International Airport, Bangkok, Thailand. In this cross-sectional study, 200 tourists (n = 200) were enrolled by convenience sampling and interviewed by structured questionnaire. Descriptive statistics, chi-square, and multinomial logistic regression were adopted in the study. Results revealed that half (50%) of the tourists were current smokers and 55 % had visited Thailand twice or more. Three quarter (76%) of tourists indicated that they would visit Thailand again even if it had a 100 % smoke-free regulation. Almost all (99%) of the tourists had supported for the smoke-free policy (partial ban and total ban), and current smokers had higher percentage of support than non-smokers. Two factors, current smoking status and knowledge level, were significantly associated with perception level. After analysis with Multinomial Logistic Regression, it was found that perception, country group, and presence of designated smoking room (DSR) were associated with
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 | 0.001 |
| 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) | 0.037 | 0.005 |
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".