Acceptability of built environment interventions to improve healthy eating and physical activity among city dwellers in Saskatchewan, Canada: THEPA findings from a local context
Bibliographic record
Abstract
Obesity and related chronic conditions present significant challenges in both national and global public health. Modifying the built environment stands as a population-level strategy to foster healthier behaviours and alleviate the burden of chronic diseases. The success of such interventions, however, hinges on their public acceptability, an aspect often overlooked. This study aimed to evaluate the level of public acceptability concerning diverse built environment interventions promoting healthy eating and physical activity within two major Canadian cities. We conducted an analysis of data gathered from 2,133 participants through a pan-Canadian survey, employing multilevel logistic regression. Interventions were categorized using the Nuffield Council on Bioethics’ ‘intervention ladder’, graded by their level of intrusiveness. Overall, individuals were more agreeable to implementing the least intrusive interventions in both food and physical activity domains. However, public support varied based on the type of intervention within and across different levels of intrusiveness. Notably, individuals self-identifying as women, Indigenous, or born outside of Canada demonstrated a higher likelihood of accepting interventions across all levels. The connection between neighborhood attributes and acceptability remained inconclusive. These locally pertinent findings carry practical implications for selecting, designing and implementing built environment interventions.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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".