Advancing Efforts to Achieve Health Equity: Equity Metrics for Health Impact Assessment Practice
Bibliographic record
Abstract
Equity is a core value of Health Impact Assessment (HIA). Many compelling moral, economic, and health arguments exist for prioritizing and incorporating equity considerations in HIA practice. Decision-makers, stakeholders, and HIA practitioners see the value of HIAs in uncovering the impacts of policy and planning decisions on various population subgroups, developing and prioritizing specific actions that promote or protect health equity, and using the process to empower marginalized communities. There have been several HIA frameworks developed to guide the inclusion of equity considerations. However, the field lacks clear indicators for measuring whether an HIA advanced equity. This article describes the development of a set of equity metrics that aim to guide and evaluate progress toward equity in HIA practice. These metrics also intend to further push the field to deepen its practice and commitment to equity in each phase of an HIA. Over the course of a year, the Society of Practitioners of Health Impact Assessment (SOPHIA) Equity Working Group took part in a consensus process to develop these process and outcome metrics. The metrics were piloted, reviewed, and refined based on feedback from reviewers. The Equity Metrics are comprised of 23 measures of equity organized into four outcomes: (1) the HIA process and products focused on equity; (2) the HIA process built the capacity and ability of communities facing health inequities to engage in future HIAs and in decision-making more generally; (3) the HIA resulted in a shift in power benefiting communities facing inequities; and (4) the HIA contributed to changes that reduced health inequities and inequities in the social and environmental determinants of health. The metrics are comprised of a measurement scale, examples of high scoring activities, potential data sources, and example interview questions to gather data and guide evaluators on scoring each metric.
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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 | 0.288 | 0.442 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".