Pattern and correlates of public support for public health interventions to reduce the consumption of sugar-sweetened beverages
Bibliographic record
Abstract
OBJECTIVE: To examine the pattern and correlates of public support for twelve public health interventions aimed at reducing sugar-sweetened beverage (SSB) consumption. DESIGN: Cross-sectional population-based survey. Respondents were recruited using a random digit dialling procedure (landline telephone) and a random selection of telephone numbers (mobile telephone). Sampling quotas were applied for age, and the sample was stratified according to administrative regions. SETTING: The province of Québec, Canada. SUBJECTS: One thousand adults aged between 18 and 64 years and able to answer the survey questionnaire in French or English. RESULTS: Support was observed for a number of public health interventions, but the more intrusive approaches were less supported. Support for taxation as well as for sale and access restriction was positively associated with the perceived relevance of the government intervention, perceived effectiveness, and perceived associations between SSB consumption and chronic diseases. Believing that SSB consumption is a personal choice and daily consumption were generally negatively associated with strong support and positively associated with strong opposition. Sparse associations between sociodemographic and socio-economic characteristics were observed, with the exception of sex and age: women were generally more likely to support the examined public health strategies, while younger respondents were less likely to express support. CONCLUSIONS: Increasing perceived effectiveness and government responsibility for addressing the issue of SSB consumption could lead to increased support for SSB interventions. Increasing the belief that SSB consumption could be associated with chronic diseases would increase support, but SSB consumers and younger individuals are expected to be resistant.
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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 | 0.009 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".