Selected health characteristics are associated with urban Canadians’ acceptability of policies promoting healthier restaurant food environments
Bibliographic record
Abstract
OBJECTIVE: The adoption of policies promoting healthier restaurant food environments is contingent on their acceptability. Limited evidence exists regarding individual characteristics associated with restaurant food environment policy acceptability, especially health-related characteristics. This study examined associations between health characteristics and restaurant food environment policy acceptability among urban Canadians. DESIGN: survey study, that is, a large pan-Canadian study on policy acceptability. For each policy, several logistic multilevel regression analyses were conducted. SETTING: Canada's seventeen most populated census metropolitan areas. PARTICIPANTS: 27 162). RESULTS: Body mass index was not associated with acceptability after adjustments for other health and sociodemographic characteristics were made. Across all policies and analyses, those reporting excellent or very good health statuses were more likely to be in complete agreement with targeted policies than those with good health statuses. For selected policies and analyses, those reporting poor health statuses were also more likely to be in complete agreement than those describing their health status as good. For all policies and analyses, both those consuming restaurant-prepared foods daily and those never consuming these foods were more likely to be in complete agreement than those consuming these foods once per week. CONCLUSIONS: More research is needed to explain discrepancies in acceptability according to health characteristics. Bringing this study's findings to the attention of policymakers may help build momentum for policy enactment.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".