Barriers to healthy urban design policies: Perspectives from the City of Regina in Saskatchewan
Bibliographic record
Abstract
The intricate interplay between urban design, health, and equity has emerged as a critical focal point in pursuit of the United Nations Sustainable Development Goal 11, which seeks to make cities inclu¬sive, safe, resilient, and sustainable. It reflects an increasing aware¬ness of the social determinants of health – a term encapsulating the socioeconomic variables that shape an individual’s living and working conditions. In other words, a person’s-built environment (the human-made surroundings within which people live and work) can either support or detract from their health. This relationship is underscored by the impact of urban design on equity. With Canada now 80% urbanized, it is not surprising that cities and urban centers have emerged as a primary arena for addressing current health challenges and safeguarding the wellbeing of residents. Therefore, by recognizing the relationship between urban design and health provides an opportunity to address gaps that contribute to health inequities. However, not all municipal actors and urban design professionals readily acknowledge their pivotal role in improving population health. To understand how key municipal policy- and decision-makers view the relationship between health and urban design, our team used the City of Regina as a case study. We interviewed 30 municipal actors from various roles and educational backgrounds within Regina’s municipal government. These interviews were analyzed using qualitative research methods and the findings were used to inform this policy paper. We identified three key categories of barriers to integrating health and equity into urban design policies: constrained policy-making environment, societal and cultural factors, and competing interests of stakeholders.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".