Conceptualizations of fairness and legitimacy in the context of Ethiopian health priority setting: Reflections on the applicability of accountability for reasonableness
Bibliographic record
Abstract
A critical element in building stronger health systems involves strengthening good governance to build capacity for transparent and fair health planning and priority setting. Over the past 20 years, the ethical framework Accountability for Reasonableness (A4R) has been a prominent conceptual guide in strengthening fair and legitimate processes of health decision-making. While many of the principles embedded within the framework are congruent with Western conceptualizations of what constitutes procedural fairness, there is a paucity in the literature that captures the degree of resonance between these principles and the views of decision makers from non-Western settings; particularly in Africa, where many countries have only recently, within the last 20-30 years, become more democratic. This paper contributes to the ethics literature by examining how Ethiopian decision makers conceptualize fair and legitimate health decision-making, and reflects on the degree of conceptual resonance between these views and the principles embedded in A4R. A qualitative case study approach from three districts in Ethiopia was undertaken. Fifty-eight decision makers from district, regional, zonal, and national levels were interviewed to describe their conceptualization of fairness and legitimacy in the district health planning process. Findings revealed that Ethiopians have a broad conception of fairness and legitimacy that while congruent with procedural justice, also aligned with principles of distributive and organizational justice. Researchers and practitioners seeking to strengthen procedural fairness in health priority setting must therefore recognize the significance of other philosophical dimensions influencing how fairness and legitimacy of health decision-making are constructed within the Ethiopian setting.
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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 | 0.072 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.103 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".