Socio-spatial inequities beyond the big city: evaluating the World Health Organization’s Urban HEART tool in a non-metropolitan context
Bibliographic record
Abstract
Where you live matters to your health. In our rapidly urbanizing world, measuring social and health inequities within cities is of increasing importance. In 2010, the World Health Organization created the Urban Health Equity Assessment and Response Tool (Urban HEART) to measure inequities within cities. It has been applied in large metropolitan centers (populations over 1 million) in low- and middle-income countries around the world. In North America, this tool was applied to Canada’s most populated city - Toronto, Ontario (population 2,731,571). However, the feasibility and utility of applying the Urban HEART tool to smaller jurisdictions has not been tested in Canada. Applying the Urban HEART tool to the city of Kingston, Ontario (population 123,798) revealed a complex story, as distinct geographic areas were simultaneously categorized as having optimal and suboptimal conditions depending on the indicator applied. While the Urban HEART provides a less granular analysis compared to other established area-based deprivation indexes, it offers unique indicators and domains that are absent from most others. In this way, the Urban HEART can stimulate conversations about the structural factors that influence population health within smaller jurisdictions, laying the foundation for evidence-based decision-making for healthier, more equitable cities regardless of size.
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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 | 0.034 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".