Institutional analysis of health system governance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is important that researchers who study health system governance have a set of collective understandings of the meanings of governance, which can then inform the methods used in research. We present an institutional framing and definition of health system governance; that is, governance refers to making, changing, monitoring and enforcing the rules that govern the demand and supply of health services. This pervasive, relational view of governance is to be preferred to approaches that focus primarily on structures of governments and health care organizations, because health system governance involves communities and service users, and because governments in many low- and middle-income countries tend to under-govern. Therefore, the study of health system governance requires institutional analysis; an approach that focuses not only on structures, but also on the rules (both formal and informal) governing demand and supply relations. Using this 'structure-relations' lens, and based on our field experience, we discuss how this focus could be applied to the three approaches to framing and studying health system governance that we identified in the literature. In order of decreasing focus on structures ('hardware') and increasing focus on relations ('software'), they are: (1) the government-centred approach, which focuses on the role of governments, above or to the exclusion of non-government health system actors; (2) the building-block approach, which focuses on the internal workings of health care organizations, and treats governance as one of the several building blocks of organizations; and (3) the institutional approach, which focuses on how the rules governing social and economic interactions are made, changed, monitored and enforced. Notably, either or both qualitative and quantitative methods may be used by researchers in efforts to incorporate the analysis of how rules determine relations among health system actors into these three approaches to health system governance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it