Association between tobacco industry denormalisation beliefs and support for tobacco endgame policies: a population-based study in Hong Kong
Bibliographic record
Abstract
OBJECTIVES: To examine the associations between tobacco industry denormalisation (TID) beliefs and support for tobacco endgame policies. METHODS: A total of 2810 randomly selected adult respondents of population-based tobacco policy-related surveys (2018-2019) were included. TID beliefs (agree vs disagree/unsure) were measured by seven items: tobacco manufacturers ignore health, induce addiction, hide harm, spread false information, lure smoking, interfere with tobacco control policies and should be responsible for health problems. Score of each item was summed up and dichotomised (median=5, >5 strong beliefs; ≤5 weak beliefs). Support for tobacco endgame policies on total bans of tobacco sales (yes/no) and use (yes/no) was reported. Associations between TID beliefs and tobacco endgame policies support across various smoking status were analysed, adjusting for sociodemographics. RESULTS: Fewer smokers (23.3%) had strong beliefs of TID than ex-smokers (48.4%) and never smokers (48.5%) (p<0.001). Support for total bans on tobacco sales (74.6%) and use (76.9%) was lower in smokers (33.3% and 35.3%) than ex-smokers (74.3% and 77.9%) and never smokers (76.0% and 78.3%) (all p values<0.001). An increase in the number of TID beliefs supported was positively associated with support for a total ban on sales (adjusted risk ratio 1.06, 95% CI 1.05 to 1.08, p<0.001) and use (1.06, 95% CI 1.05 to 1.07, p<0.001). The corresponding associations were stronger in smokers than non-smokers (sales: 1.87 vs 1.25, p value for interaction=0.03; use: 1.78 vs 1.21, p value for interaction=0.03). CONCLUSION: Stronger TID belief was associated with greater support for total bans on tobacco sales and use. TID intervention may increase support for tobacco endgame, especially in current 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".