Transversal analysis of public policies on user fees exemptions in six West African countries
Bibliographic record
Abstract
BACKGROUND: While more and more West African countries are implementing public user fees exemption policies, there is still little knowledge available on this topic. The long time required for scientific production, combined with the needs of decision-makers, led to the creation in 2010 of a project to support implementers in aggregating knowledge on their experiences. This article presents a transversal analysis of user fees exemption policies implemented in Benin, Burkina Faso, Mali, Niger, Togo and Senegal. METHODS: This was a multiple case study with several embedded levels of analysis. The cases were public user fees exemption policies selected by the participants because of their instructive value. The data used in the countries were taken from documentary analysis, interviews and questionnaires. The transversal analysis was based on a framework for studying five implementation components and five actors' attitudes usually encountered in these policies. RESULTS: The analysis of the implementation components revealed: a majority of State financing; maintenance of centrally organized financing; a multiplicity of reimbursement methods; reimbursement delays and/or stock shortages; almost no implementation guides; a lack of support measures; communication plans that were rarely carried out, funded or renewed; health workers who were given general information but not details; poorly informed populations; almost no evaluation systems; ineffective and poorly funded coordination systems; low levels of community involvement; and incomplete referral-evacuation systems. With regard to actors' attitudes, the analysis revealed: objectives that were appreciated by everyone; dissatisfaction with the implementation; specific tensions between healthcare providers and patients; overall satisfaction among patients, but still some problems; the perception that while the financial barrier has been removed, other barriers persist; occasionally a reorganization of practices, service rationing due to lack of reimbursement, and some overcharging or shifting of resources. CONCLUSIONS: This transversal analysis confirms the need to assign a great deal of importance to the implementation of user fees exemption policies once these decisions have been taken. It also highlights some practices that suggest avenues of future research.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".