The health equity measurement framework: a comprehensive model to measure social inequities in health
Bibliographic record
Abstract
BACKGROUND: Despite the wealth of frameworks on social determinants of health (SDOH), two current limitations include the relative superficial description of factors affecting health and a lack of focus on measuring health equity. The Health Equity Measurement Framework (HEMF) addresses these gaps by providing a more encompassing view of the multitude of SDOH and drivers of health service utilisation and by guiding quantitative analysis for public health surveillance and policy development. The objective of this paper is to present the HEMF, which was specifically designed to measure the direct and indirect effects of SDOH to support improved statistical modelling and measurement of health equity. METHODS: Based on a framework synthesis, the HEMF development involved initially integrating theoretical components from existing SDOH and health system utilisation frameworks. To further develop the framework, relevant publications on SDOH and health equity were identified through a literature review in major electronic databases. White and grey literatures were critically reviewed to identify strengths and gaps in the existing frameworks in order to inform the development of a unique health equity measurement framework. Finally, over a two-year period of consultation, scholars, health practitioners, and local policy influencers from municipal and provincial governments provided critical feedback on the framework regarding its components and causal relationships. RESULTS: This unified framework includes the socioeconomic, cultural, and political context, health policy context, social stratification, social location, material and social circumstances, environment, biological factors, health-related behaviours and beliefs, stress, quality of care, and healthcare utilisation. Alongside the HEMF's self-exploratory diagram showing the causal pathways in-depth, a number of examples are provided to illustrate the framework's usefulness in measuring and monitoring health equity as well as informing policy-making. CONCLUSIONS: The HEMF highlights intervention areas to be influenced by strategic public policy for any organisation whose purview has an effect on health, including helping non-health sectors (such as education and labour) to better understand how their policies influence population health and perceive their role in health equity promotion. The HEMF recognises the complexity surrounding the SDOH and provides a clear, overarching direction for empirical work on health equity.
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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 | 0.036 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".