Strategies and indicators to integrate health equity in health service and delivery systems in high-income countries: a scoping review
Bibliographic record
Abstract
OBJECTIVE: The objective of this review was to describe how health service and delivery systems in high-income countries define and operationalize health equity. A secondary objective was to identify implementation strategies and indicators being used to integrate and measure health equity. INTRODUCTION: To improve the health of populations, a population health and health equity approach is needed. To date, most work on health equity integration has focused on reducing health inequities within public health, health care delivery, or providers within a health system, but less is known about integration across the health service and delivery system. INCLUSION CRITERIA: This review included academic and gray literature sources that described the definitions, frameworks, level of integration, strategies, and indicators that health service and delivery systems in high-income countries have used to describe, integrate, and/or measure health equity. Sources were excluded if they were not available in English (or a translation was not available), were published before 1986, focused on strategies that were not implemented, did not provide health equity indicators, or featured strategies that were implemented outside the health service or delivery systems (eg, community-based strategies). METHODS: This review was conducted in accordance with the JBI methodology for scoping reviews. Titles and abstracts were screened for eligibility followed by a full-text review to determine inclusion. The information extracted from the included studies consisted of study design and key findings, such as health equity definitions, strategies, frameworks, level of integration, and indicators. Most data were quantitatively tabulated and presented according to 5 secondary review questions. Some findings (eg, definitions and indicators) were summarized using qualitative methods. Most findings were visually presented in charts and diagrams or presented in tabular format. RESULTS: Following review of 16,297 titles and abstracts and 824 full-text sources, we included 122 sources (108 scholarly and 14 gray literature) in this scoping review. We found that health equity was inconsistently defined and operationalized. Only 17 sources included definitions of health equity, and we found that both indicators and strategies lacked adequate descriptions. The use of health equity frameworks was limited and, where present, there was little consistency or agreement in their use. We found that strategies were often specific to programs, services, or clinics, rather than broadly applied across health service and delivery systems. CONCLUSIONS: Our findings suggest that strategies to advance health equity work are siloed within health service and delivery systems, and are not currently being implemented system-wide (ie, across all health settings). Healthy equity definitions and frameworks are varied in the included sources, and indicators for health equity are variable and inconsistently measured. Health equity integration needs to be prioritized within and across health service and delivery systems. There is also a need for system-wide strategies to promote health equity, alongside robust accountability mechanisms for measuring health equity. This is necessary to ensure that an integrated, whole-system approach can be consistently applied in health service and delivery systems internationally. REVIEW REGISTRATION: DalSpace dalspace.library.dal.ca/handle/10222/80835.
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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 | 0.051 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.022 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".