Barriers to integration of health and equity into urban design policies in Regina, Saskatchewan
Bibliographic record
Abstract
Although there is extensive literature on the impact of urban design on health, little is known about the barriers to integrating health into urban design policies. As cities increasingly lead efforts to improve health equity and population health, understanding the perspectives and experiences of municipal actors on health and equity is essential. To address this gap, we conducted semi-structured interviews with 30 stakeholders engaged with urban design policy- and decision-making at the City of Regina in Saskatchewan, Canada. We analysed our data using a qualitative thematic framework. Our research uncovered a lack of shared understanding of health among municipal actors. Interviewees identified several barriers to integrating health and equity in urban design policies, including inaccessibility of evidence; insufficient resourcing; fragmented governance structure; limited legal power of local governments in Canada; a deeply ingrained culture of individualism and lack of representation. Our findings underscore the importance of adopting an integrated and holistic approach for healthy and equitable urban design. As urbanization continues to bring a greater share of the world's population into urban areas, it is crucial to understand how municipal governance can foster environments that promote residents' well-being.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".