Assessing health equity inconsistencies in the World Health Organization’s Urban HEART initiative: findings from key informant interviews
Bibliographic record
Abstract
BACKGROUND: To date, no studies have assessed how the World Health Organization's (WHO) work operationalizes health equity in practice. To fill the gap, this study investigates the WHO's Urban Health Equity Assessment and Response Tool (Urban HEART) that focuses on assessing and responding to inequities within cities. This qualitative research answers the question: "how does Urban HEART and associated policy and practice align (or not) with inconsistent approaches to health in/equity?" In other words, asking if past findings from investigating WHO key texts also transpire into Urban HEART and its practices. METHODS: Purposive sampling was employed to undertake synchronous electronic interviews with key informants to glean a multi-faceted perspective of how equity was operationalized through Urban HEART. Data was collected from 18 key informants who had diverse experiences with Urban HEART. RESULTS: The results of this study provided insights on how the WHO's Urban HEART fares with respect to the three inconsistencies. For the first inconsistency, measurement, Urban HEART was evaluated to measure inequities across districts and neighbourhoods, but not inter-city, demonstrating alignment with WHO texts discussing measurement across groups and not individuals. For the second inconsistency, the goals or approaches sought in striving for health equity, despite Urban HEART presenting "three main approaches to reduce health inequities," informants expressed the most alignment of Urban HEART action with only one of these approaches ("targeting disadvantaged population groups or social classes"). However, informants also shared how actions taken as part of Urban HEART largely aligned with "striving for a baseline level of health for all," which is not explicitly specified by the WHO as a main approach. And lastly, in assessing the third inconsistency of whether Urban HEART aligned with addressing inequity through focusing on socioeconomic status/position versus broader social determinants of health, Urban HEART was strongly aligned with the latter. CONCLUSIONS: This study presents disconnects between WHO's intentions and actions that followed from Urban HEART. Moving forward, it would be important to discuss goals or approaches sought, prior to any global health initiatives, whether explicitly focused on health equity or not.
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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 | 0.091 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".