Linking evidence to action on social determinants of health using Urban HEART in the Americas.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the experience of select cities in the Americas using the Urban Health Equity Assessment and Response Tool (Urban HEART) launched by the World Health Organization in 2010 and to determine its utility in supporting government efforts to improve health equity using the social determinants of health (SDH) approach. METHODS: The Urban HEART experience was evaluated in four cities from 2010-2013: Guarulhos (Brazil), Toronto (Canada), and Bogotá and Medellín (Colombia). Reports were submitted by Urban HEART teams in each city and supplemented by first-hand accounts of key informants. The analysis considered each city's networks and the resources it used to implement Urban HEART; the process by which each city identified equity gaps and prioritized interventions; and finally, the facilitators and barriers encountered, along with next steps. RESULTS: In three cities, local governments spearheaded the process, while in the fourth (Toronto), academia initiated and led the process. All cities used Urban HEART as a platform to engage multiple stakeholders. Urban HEART's Matrix and Monitor were used to identify equity gaps within cities. While Bogotá and Medellín prioritized among existing interventions, Guarulhos adopted new interventions focused on deprived districts. Actions were taken on intermediate determinants, e.g., health systems access, and structural SDH, e.g., unemployment and human rights. CONCLUSIONS: Urban HEART provides local governments with a simple and systematic method for assessing and responding to health inequity. Through the SDH approach, the tool has provided a platform for intersectoral action and community involvement. While some areas of guidance could be strengthened, Urban HEART is a useful tool for directing local action on health inequities, and should be scaled up within the Region of the Americas, building upon current experience.
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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 | 0.060 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.009 |
| 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".