Addressing health equity within the implementation of health system reforms: A scoping review
Bibliographic record
Abstract
Abstract Background Health equity is a commonly asserted goal of health systems. However, there is limited understanding on appropriate equity-promoting strategies within system reform initiatives. To address this knowledge gap, we conducted a scoping review to (1) identify and characterise strategies that promote health equity during the implementation of health reform initiatives; and (2) reflect on implications for reform implementation and research. Methods Using scoping review methodology, we systematically searched peer-reviewed literature from 2013-2022. A comprehensive search strategy captured literature inclusive of four domains: (1) health equity; (2) implementation; (3) health system; and (4) reform, policy, or theories. Thematic analysis was conducted, and findings reported according to the PRISMA checklist. Results Of 10,999 identified articles after duplicates were removed, 69 articles met the inclusion criteria. A vast number of health equity promoting themes derived from individual strategies was identified, with a median of 10 strategies (interquartile range 7, 15) per article. Two a priori conditions were observed: (1) the need for health equity promoting strategies to be made explicit, and (2) the need for alignment and complementarity of strategies at community, regional, state, and national health system levels. Themes identified, such as accountability, leadership commitment, shared power, adaptability, and trust, present opportunities to support strategy selection and alignment across health system levels. Underpinning strategies were present across a range of governance domains, although emphasis varied by health system level. Conclusions The review recognises that different strategies may be applicable in different contexts; however, it may help to be explicit toward health equity strategies and align across health system levels. In future, reform implementation reporting should be made more consistent to promote a shared language. Key messages • A comprehensive review identified two a priori conditions to support equity promoting strategies within reform implementation; being explicit towards equity, and alignment across system levels. • Our research identified health equity-promoting strategies within health reform implementation. Common across system levels included accountability, leadership commitment, shared power and monitoring.
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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 | 0.059 | 0.208 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.025 | 0.028 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".