Health Equity in All Urban Policies: A Case Study of Richmond, California
Bibliographic record
Abstract
Local governments working in partnership with communities can institutionalize practices that promote health equity. We offer a case study of how one city in the US is implementing Health in All Policies (HiAP) with the explicit aim of promoting health equity. We use participant observations, original document reviews and interviews to describe how Richmond, California, is building new partnerships, programs and practices with community-based organizations and within government itself as part of the implementation of its HiAP Ordinance. We also report on indicators that were identified by community and government stakeholders for tracking progress toward improving place-based determinants of population health. We find that the responsibility for implementing Richmond's HiAP Ordinance rests on a new institution within local government and this entity is building new partnerships, promoting innovative policies and augmenting practices toward greater health equity. We also reveal how city governments and community partners can collaboratively track progress toward health equity using locally gathered data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".