96:oral Equity, justice, and social values in priority setting: a qualitative study of resource allocation criteria for global donor organizations working in low-income countries
Bibliographic record
Abstract
Background There is increasing acceptance of the importance of social values like equity and fairness in health care priority setting (PS). However, equity is difficult to define; it means different things to different people. How equity is understood in theory, may not align with how it is operationalized. There is limited literature on how development assistance partner organizations (DAP) conceptualize and operationalize equity in their health care prioritization decisions for low-income countries (LIC). We explore whether and how equity is a consideration in DAP’s PS processes. Methods A qualitative study involving 35 in-depth interviews with DAPs involved in health-system PS for LICs and review of their respective webpages. Results While several PS criteria were identified, direct articulation of equity as an explicit criterion was lacking. However, equity was implied, by some responses, through prioritizing of vulnerable populations. Where mentioned, respondents discussed the difficulties of operationalizing equity, since vulnerability is associated with several, competing factors including gender, age, geography, and income. Some respondents suggested that equity could be operationalized through organizations’ lack of support for programs that reinforce pre-existing inequities. Although several organizations’ webpages identify addressing inequities as a guiding principle, they varied in their discussion its operationalization. While intersectionalities in vulnerabilities complicate its operationalization, if the various organizations explicitly articulate their equity focus, each organization may concentrate on different dimensions of vulnerability. Thus, all organizations will contribute to achieving equity in all the relevant dimensions. Conclusions Since most DAPs support some form of equity, we highlight a need for an internationally recognized framework that recognizes the intersectionalities of vulnerability, for mainstreaming and operationalizing equity in DAP priority setting and resource allocation. This framework will support consistent conceptualization and operationalization of equity in global health programs. The degree to which equity is actually integrated in these programs merits further study.
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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 | 0.043 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".