Development of Health Equity Indicators in Primary Health Care Organizations Using a Modified Delphi
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to develop a core set of indicators that could be used for measuring and monitoring the performance of primary health care organizations' capacity and strategies for enhancing equity-oriented care. METHODS: Indicators were constructed based on a review of the literature and a thematic analysis of interview data with patients and staff (n = 114) using procedures for qualitatively derived data. We used a modified Delphi process where the indicators were circulated to staff at the Health Centers who served as participants (n = 63) over two rounds. Indicators were considered part of a priority set of health equity indicators if they received an overall importance rating of>8.0, on a scale of 1-9, where a higher score meant more importance. RESULTS: Seventeen indicators make up the priority set. Items were eliminated because they were rated as low importance (<8.0) in both rounds and were either redundant or more than one participant commented that taking action on the indicator was highly unlikely. In order to achieve health care equity, performance at the organizational level is as important as assessing the performance of staff. Two of the highest rated "treatment" or processes of care indicators reflects the need for culturally safe and trauma and violence-informed care. There are four indicators that can be used to measure outcomes which can be directly attributable to equity responsive primary health care. DISCUSSION: These indicators and subsequent development of items can be used to measure equity in the domains of treatment and outcomes. These areas represent targets for higher performance in relation to equity for organizations (e.g., funding allocations to ongoing training in equity-oriented care provision) and providers (e.g., reflexive practice, skill in working with the health effects of trauma).
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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 | 0.211 | 0.162 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| 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".