Characterizing ‘health equity’ as a national health sector priority for maternal, newborn, and child health in Ethiopia
Bibliographic record
Abstract
Background: The pursuit of health equity is a priority in Ethiopia, especially with regards to maternal, newborn, and child health (MNCH). To date, there has been little characterization of the ‘problem’ of health inequity, and the normative assumptions implicit in the representation of the problem. Yet, such insights have implications for shaping the framing, incentivization, and implementation of health policies and their wider impact.Objective: In this article, we characterize how health (in)equity is represented as a policy issue, how this representation came about, and the underlying assumptions.Methods: We draw from Bacchi’s ‘what is the problem represented to be’ approach to explore how national-level actors in the health sector constitute the problem. The data for our analysis encompass 23 key informant interviews with national health sector actors working in leadership positions on MNCH in Ethiopia, and six policy documents. Findings were derived from thematic and content analysis.Results: Health inequity is a normalized and inevitable concern that is regarded as actionable (can be altered) but not fully resolvable (can never be fully achieved). Operationally, health equity is viewed as a technocratic matter, reflected in the widespread use of metrics to motivate and measure progress. These representations are shaped by Ethiopia’s rapid expansion of health services into rural areas during the 2000s leading to the positive international attention and funding the country received for improved MNCH indicators. Expanding the coverage and efficiency of health service provision, especially in rural areas, is associated with economic productivity.Conclusion: The metrication of health equity may detract from the fairness, justice, and morality underpinnings of the concept. The findings of this study point to the implications of global pressures in terms of maximizing health investments, and call into question how social, political, and economic determinants of health are addressed through broader development agendas.
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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 | 0.014 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".