Capabilities, opportunities, motivations, and practices of different sector professionals working on community environments to improve health
Bibliographic record
Abstract
OBJECTIVE: With rising healthcare costs in Canada from chronic conditions, individual behaviour change interventions in the clinical settings need to be complemented by a determinants of health approach, where multi-sector professionals assist in the creation of healthier community environments. This study sought to gain insights into capabilities, opportunities, motivations, and behaviours (COM-B) of Canadian multi-sector professionals for working together to improve built environments (BE) for health. METHODS: A cross-sectional study was conducted with 61 multi-sector professionals. A 49-item questionnaire measuring constructs of COM-B for healthy BE practices was administered. RESULTS: Public health (PH) professionals were more motivated by personal interest/values in healthy BE and the presence of scientific evidence on BE design health impacts as compared with planning and policy/program development (PPD) professionals. Planning professionals were more likely to be motivated by healthy BE legislation/regulations/codes than PPD professionals. The practice of taking responsibility for the inclusion of healthy features into BE designs was reported more often by planning and other professionals compared to PH professionals. Results trended towards significance for opportunities as a predictor of healthy BE practices among all professionals. CONCLUSION: Though motivators vary among different sector professionals, opportunities may be the most important driver of healthy BE practices and potentially a target to improve multi-sector professional practices in Canada. Future research should confirm findings of this first study of professional practice drivers guided by a theoretical behaviour change framework.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".