Practicing governance towards equity in health systems: LMIC perspectives and experience
Bibliographic record
Abstract
The unifying theme of the papers in this series is a concern for understanding the everyday practice of governance in low- and middle-income country (LMIC) health systems. Rather than seeing governance as a normative health system goal addressed through the architecture and design of accountability and regulatory frameworks, these papers provide insights into the real-world decision-making of health policy and system actors. Their multiple, routine decisions translate policy intentions into practice - and are filtered through relationships, underpinned by values and norms, influenced by organizational structures and resources, and embedded in historical and socio-political contexts. These decisions are also political acts - in that they influence who accesses benefits and whose voices are heard in decision-making, reinforcing or challenging existing institutional exclusion and power inequalities. In other words, the everyday practice of governance has direct impacts on health system equity.The papers in the series address governance through diverse health policy and system issues, consider actors located at multiple levels of the system and draw on multi-disciplinary perspectives. They present detailed examination of experiences in a range of African and Indian settings, led by authors who live and work in these settings. The overall purpose of the papers in this series is thus to provide an empirical and embedded research perspective on governance and equity in health systems.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".