Setting performance-based financing in the health sector agenda: a case study in Cameroon
Bibliographic record
Abstract
BACKGROUND: More than 30 countries in sub-Saharan Africa have introduced performance-based financing (PBF) in their healthcare systems. Yet, there has been little research on the process by which PBF was put on the national policy agenda in Africa. This study examines the policy process behind the introduction of PBF program in Cameroon. METHODS: The research is an explanatory case study using the Kingdon multiple streams framework. We conducted a document review and 25 interviews with various types of actors involved in the policy process. We conducted thematic analysis using a hybrid deductive-inductive approach for data analysis. RESULTS: By 2004, several reports and events had provided evidence on the state of the poor health outcomes and health financing in the country, thereby raising awareness of the situation. As a result, decision-makers identified the lack of a suitable health financing policy as an important issue that needed to be addressed. The change in the political discourse toward more accountability made room to test new mechanisms. A group of policy entrepreneurs from the World Bank, through numerous forms of influence (financial, ideational, network and knowledge-based) and building on several ongoing reforms, collaborated with senior government officials to place the PBF program on the agenda. The policy changes occurred as the result of two open policy windows (i.e. national and international), and in both instances, policy entrepreneurs were able to couple the policy streams to effect change. CONCLUSION: The policy agenda of PBF in Cameroon underlined the importance of a perceived crisis in the policy reform process and the advantage of building a team to carry forward the policy process. It also highlighted the role of other sources of information alongside scientific evidence (eg.: workshop and study tour), as well as the role of previous policies and experiences, in shaping or influencing respectively the way issues are framed and reformers' actions and choices.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".