What is the role of health equity tools in large-systems transformation?
Bibliographic record
Abstract
Background Growing health inequities between affluent and disadvantaged groups in society are of specific concern for people working in public health systems. To address health inequity there is a need to reorient health systems toward health equity. Tools or frameworks provide one strategy for reorientation of health systems but many lack guidance on practical use or information on theoretical foundations. We define tools as documents that assess, promote, or measure health equity and provide a set of steps, questions, or a framework that practitioners can follow to achieve these goals. Methods We applied the general method of an environmental scan to conduct our search for sources and documents on health equity tools, as the first phase before developing practical and theoretical criteria for assessing health equity tools. We conducted a systematic search of published and grey literature up to 2011 through eight online data bases and through a search of major equity-related websites dedicated to study and promotion of health equity. Results We reviewed 593 documents from peer-reviewed or grey English-language literature and identified 35 health equity tools that span a range of users. These tools encompass a broad range of purposes that incorporate considerations of health equity into policy, program development, and evaluation. They also vary on determinants of health inequities including the built environment, ethno-cultural considerations, gender and the importance of empowerment of disadvantaged groups. There is a lack of documents that critically assess tools in terms of what tool for whom and what purpose and a need to understand how to identify practical considerations and the theoretical basis of tools. Conclusions While a number of tools have been applied, only two were reported in the literature as being evaluated. The majority of tools are lacking guidance on practical use and theoretical underpinnings, restricting the ability of practitioners to identify the right tool for the right purpose. We recognize the need for further evaluation of health equity tools and discuss potential for system transformation and orientation through the use of such tools. Key message Health equity tools can be useful for systems transformation, particularly with appropriate guidance for practical use and knowledge of the theoretical basis.
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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 | 0.155 | 0.185 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.007 | 0.065 |
| Scholarly communication | 0.039 | 0.063 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 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".