Probing key informants’ views of health equity within the World Health Organization’s Urban HEART initiative
Bibliographic record
Abstract
Abstract To date, no studies have assessed how those involved in the World Health Organization’s (WHO) work understand the concept of health equity. To fill the gap, this research poses the question, “how do Urban Health Equity Assessment and Response Tool (Urban HEART) key informants understand the concept of health equity?”, with Urban HEART being selected given the focus on health equity. To answer this question, this study undertakes synchronous electronic interviews with key informants to assess how they understand health equity within the context of Urban HEART. Key findings demonstrate that: (i) equity is seen as a core value and inequities were understood to be avoidable, systematic, unnecessary, and unfair; (ii) there was a questionable acceptance of need to act, given that political sensitivity arose around acknowledging inequities as “unnecessary”; (iii) despite this broader understanding of the key aspects of health inequity, the concept of health equity was seen as vague; (iv) the recognized vagueness inherent in the concept of health equity may be due to various factors including country differences; (v) how the terms “health inequity” and “health inequality” were used varied drastically; and (vi) when speaking about equity, a wide range of aspects emerged. Moving forward, it would be important to establish a shared understanding across key terms and seek clarification, 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 | 0.050 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".