A scoping review of theories and conceptual frameworks used to analyse health financing policy processes in sub-Saharan Africa
Bibliographic record
Abstract
Health financing policies are critical policy instruments to achieve Universal Health Coverage, and they constitute a key area in policy analysis literature for the health policy and systems research (HPSR) field. Previous reviews have shown that analyses of policy change in low- and middle-income countries are under-theorised. This study aims to explore which theories and conceptual frameworks have been used in research on policy processes of health financing policy in sub-Saharan Africa and to identify challenges and lessons learned from their use. We conducted a scoping review of literature published in English and French between 2000 and 2017. We analysed 23 papers selected as studies of health financing policies in sub-Saharan African countries using policy process or health policy-related theory or conceptual framework ex ante. Theories and frameworks used alone were from political science (35%), economics (9%) and HPSR field (17%). Thirty-five per cent of authors adopted a 'do-it-yourself' (bricolage) approach combining theories and frameworks from within political science or between political science and HPSR. Kingdon's multiple streams theory (22%), Grindle and Thomas' arenas of conflict (26%) and Walt and Gilson's policy triangle (30%) were the most used. Authors select theories for their empirical relevance, methodological rational (e.g. comparison), availability of examples in literature, accessibility and consensus. Authors cite few operational and analytical challenges in using theory. The hybridisation, diversification and expansion of mid-range policy theories and conceptual frameworks used deductively in health financing policy reform research are issues for HPSR to consider. We make three recommendations for researchers in the HPSR field. Future research on health financing policy change processes in sub-Saharan Africa should include reflection on learning and challenges for using policy theories and frameworks in the context of HPSR.
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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 | 0.033 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.045 | 0.047 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".