Conceptual approaches in combating health inequity: A scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: What are the different ways in which health equity can be sought through policy and programs? Although there is a central focus on health equity in global and public health, we recognize that stakeholders can understand health equity as taking different approaches and that there is not a single conceptual approach. However, information on conceptual categories of actions to improve health equity and/or reduce health inequity is scarce. Therefore, this study asks the research question: "what conceptual approaches exist in striving for health equity and/or reducing health inequity?" with the aim of presenting a comprehensive overview of approaches. METHODS: A scoping review will be undertaken following the PRISMA guidelines for Scoping Reviews (PRISMA-ScR) and in consultation with a research librarian. Both the peer-reviewed and grey literatures will be searched using: Ovid MEDLINE, Scopus, PAIS Index (ProQuest), JSTOR, Canadian Public Documents Collection, the World Health Organization IRIS (Institutional Repository for Information Sharing), and supplemented by a Google Advanced Search. Screening will be conducted by two independent reviewers and data will be charted, coded, and narratively synthesized. DISCUSSION: We anticipate developing a foundational document compiling categories of approaches and discussing the nuances inherent in each conceptualization to promote clarified and united action.
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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 | 0.205 | 0.159 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.026 | 0.024 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.081 | 0.021 |
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".