Scoping review of the World Health Organization’s underlying equity discourses: apparent ambiguities, inadequacy, and contradictions
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Given the heightened rhetorical prominence the World Health Organization has afforded to equity in the past half-century, it is important to better understand how equity has been referred to and its conceptual underpinning, which may have broader global implications. ELIGIBILITY CRITERIA: Articles were included if they met inclusion criteria - chiefly the explicit discussion of the WHO's concept of health equity, for example in terms of conceptualization and/or definitions. Articles which mentioned health equity in the context of WHO's programs, policies, and so on, but did not discuss its conceptualization or definition were excluded. SOURCES OF EVIDENCE: We focused on peer-reviewed literature by scanning Ovid MEDLINE and SCOPUS databases, and supplementing by hand-search. RESULTS: Results demonstrate the WHO has held - and continues to hold - ambiguous, inadequate, and contradictory views of equity that are rooted in different theories of social justice. CONCLUSIONS: Moving forward, the WHO should revaluate its conceptualization of equity and normative position, and align its work with Amartya Sen's Capabilities Approach, as it best encapsulates the broader views of the organization. Further empirical research is needed to assess the WHO interpretations and approaches to equity.
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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 | 0.088 | 0.289 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.056 | 0.062 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".