What criteria guide national entrepreneurs’ policy decisions on user fee removal for maternal health care services? Use of a best–worst scaling choice experiment in West Africa
Bibliographic record
Abstract
OBJECTIVE: Several countries in sub-Saharan Africa have implemented policies to remove or reduce user fees. Our aim was to identify criteria guiding such decisions among national policy entrepreneurs, those who link up problem definition, solution development and political processes. METHODS: We administered a best-worst scaling (BWS) experiment to 89 policy entrepreneurs, asking them to identify the most and the least important criteria on a series of predefined sets. Sets were compiled using a Balance Incomplete Block Design which generated random combinations of all 11 criteria included in the experiment. In turn, those had emerged from a prior set of focus group discussions organized among policy entrepreneurs. Ordered logit models were used to investigate the value of single criteria as well as heterogeneity of preferences. RESULTS: Political commitment was identified as the most important criterion guiding policy decisions on user fee abolition or reduction to the overall sample, but particularly so for more experienced respondents aged over 50 years. International pressure and donor money were identified as least important while equity and institutional capacity were deemed of relatively little importance. Respondents more involved in advising on policy than on formulating policy rated economic issues such as financial sustainability and cost-effectiveness as less important. CONCLUSIONS: It is feasible to apply BWS experiments in low-income countries, although whether the technique can be adjusted to elicit preferences among non-literate respondents in these settings is unclear.
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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 | 0.035 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".