Health equity and distributive justice: views of high-level African policymakers
Bibliographic record
Abstract
Health equity matters, but there is no universally accepted definition of this or associated terms, such as inequities, inequalities, and disparities. Given the flexibility of these terms, investigating how policymakers understand them is important to observe priorities and perhaps course correct. Accordingly, this study analyzed the perceptions high-level policymakers within the WHO African Region. An online survey was distributed to attendees of the WHO's Fifth Health Sector Directors' Policy and Planning Meeting for the WHO African Region by email. After responses were collected, both inductive and deductive coding were applied. Inductive coding was undertaken to glean central concepts from free-form responses on understandings of health equity and deductive coding was used to assess alignment with four theories of distributive justice using a coding framework. In analyzing central concepts, three became apparent: access to health services and/or health care, financial protection, and recognizing subgroups. And when we investigated alignment with theory, most respondents' understandings of health equity aligned with Rawls' 'Theory of Justice' (95%). Of these responses, 70% were exclusively aligned with Rawls' 'Theory of Justice' and 30% aligned also with another theory (this 30% was split 55% utilitarianism and 45% Sen's Capabilities Approach). Respondent understandings of health equity showed limited alignment with other theories of distributive justice, which were: utilitarianism (n = 7/39; 17.95%), Sen's Capabilities Approach (n = 5/39; 12.82%), and libertarianism (n = 2/39; 5.13%). Our study demonstrates that alignment with certain theories is tied to specific themes (i.e., theoretical underpinnings may guide policymakers to favour certain policy approaches). For instance, a utilitarian-minded policymaker may be focused on a widespread vaccination campaign, whereas a Rawlsian-aligned policymaker may focus on a targeted approach to reach communities that have lower vaccination rates, and a Senian-aligned policymaker may focus on health literacy programs targeted at addressing vaccine-hesitant individuals within communities with lower vaccination rates. These findings can guide high-level policymakers and international organizations to optimize decision-making by clarifying ethical alternatives.
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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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".