Free healthcare for some, fee-paying for the rest: adaptive practices and ethical issues in rural communities in the district of Boulsa, Burkina Faso
Bibliographic record
Abstract
In Burkina Faso, in July 2016, user fees were removed at all public healthcare facilities, but only for children under 60 months of age and for “mothers”, i.e. for reproductive care. This study was conducted in five rural communities in Boulsa District (Burkina Faso) (1) to understand the perceptions and practices of stakeholders regarding compliance with eligibility criteria for free care and (2) to explore the ethical tensions that may have resulted from this policy. Semi-directed individual interviews (n = 20) were conducted with healthcare personnel and mothers of young children. Interviews were recorded and transcribed, and a thematic content analysis was conducted. The study reveals the presence of practices to circumvent strict compliance with the eligibility criteria for free access. These include hiding the exact age of children over 60 months and using eligible persons for the benefit of others. These practices result from ethical and economic tensions experienced by the beneficiaries. They also raise dilemmas among healthcare providers, who have to enforce compliance with the eligibility criteria while realizing the households’ deprivation. Informal adjustments are introduced at the community level to reconcile the healthcare providers’ dissonance. Local reinvention mechanisms help in overcoming ethical tensions and in implementing the policy.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| 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".