Sociodemographic characteristics help predict Canadian urbanites’ acceptability of restaurant food environment policies
Bibliographic record
Abstract
Introduction: Public acceptability of policies aiming to improve the healthfulness of the restaurant food environment is key to their successful implementation. Yet, the acceptability of these policies remains ambiguous, especially across diverse population groups. This study aims to examine associations between sociodemographic characteristics and acceptability levels of three restaurant food environment policies of varying degrees of intrusiveness across 17 urban Canadian jurisdictions. Methods: = 27,162). To account for potential jurisdictional differences in acceptability, for each policy, multilevel logistic regression models were developed. Results: Results indicated that, on average, those in complete agreement with the implementation of the targeted policies represented 20.3%-26.9% of participants, depending on the policy. Acceptability varied according to policy intrusiveness, jurisdiction, and participants' sociodemographic characteristics. Women, individuals with household incomes of <$40,000/year, immigrants from a high-income country other than Canada, and Indigenous peoples were more likely to express complete agreement with all policies, versus men, participants with household incomes of $40,000-$79,999/year, Canadian-born individuals, and non-Indigenous individuals. A lower likelihood of expressing complete agreement with all policies was observed for those with a $80,000-$119,999/year household income, versus those with a $40,000-$79,999/year household income. For selected policies and models, other sociodemographic characteristics (i.e., age, education, and being born in a low-or middle-income country) predicted acceptability. The examined sociodemographic characteristics did not explain jurisdictional differences in acceptability. Discussion: Understanding jurisdictional differences in acceptability merits further research. Policy implications involve engaging diverse sociodemographic groups in conversations about acceptable ways in which their restaurant food environment could be rendered more healthful.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".