Strategies and indicators to address health equity in health service and delivery systems: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The purpose of this review is to describe how health service and delivery systems support health equity, and to identify strategies and indicators being used to measure health equity. INTRODUCTION: It is widely acknowledged that a population health and equity approach is needed to improve the overall health of the population. The health service and delivery system plays an important role in this approach. Despite this, system transformation to address health inequities has been slow. This is due, in part, to the lack of evidence-based guidance on how health service and delivery systems can address and measure health equity integration. Most studies focus on health equity integration in the public health sector at a provincial or national level, but less is known about integration within the health service and delivery system. More information is needed to understand how that transformation is occurring, or could occur, to make a meaningful contribution toward improving population health outcomes. INCLUSION CRITERIA: This scoping review will identify studies that describe the strategies and indicators that health service and delivery systems are using to integrate health equity and how progress is measured. Evidence from qualitative, quantitative, mixed method studies, and gray literature will be included. METHODS: This review will be conducted in accordance with JBI methodology for scoping reviews. A comprehensive search strategy, developed with a librarian scientist, will be used to identify relevant sources. Titles, abstracts, and full texts will be evaluated against inclusion criteria. Information will be extracted by two independent reviewers. Data will be synthesized and presented narratively, with tables and figures where appropriate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.158 | 0.121 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.029 | 0.026 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.052 | 0.016 |
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".