Combining user fees exemption with training and supervision helps to maintain the quality of drug prescriptions in Burkina Faso
Bibliographic record
Abstract
To improve access to health care services, an intervention was implemented in Burkina Faso granting full exemption from user fees. Two further components, staff training and supervision, were added to support the intervention. Our aim in this study was to examine how this tripartite intervention affected the quality of drug prescriptions. Using a mixed methodology, we first conducted an interrupted time series over 24 months. Nine health centres were studied that had previously undergone a process analysis. A total of 14 956 prescriptions for children 0-4 years old were selected by interval sampling from the visit registries from 1 year before to 1 year after the intervention's launch. We then interviewed 14 prescribers. We used three World Health Organization (WHO) indicators to assess drug prescription quality. Analysis was carried out using linear regression and logistic regression. The prescribers' statements underwent content analysis, to understand their perceptions and changes in their practice since the subsidy's introduction. One effect of the intervention was a reduced use of injections (odd ratio (OR) = 0.28 [0.17; 0.46]) in cases of acute lower respiratory tract infections (ALRTI) without comorbidity. Another was a reduction in the inappropriate use of antibiotics in malaria without comorbidity (OR = 0.48 [0.33; 0.70]). The average number of drugs prescribed also decreased (coefficient = -0.14 [-0.20; -0.08]) in cases of ALRTI without comorbidity. The prescribers reported that their practices were either maintained or improved. The user fees exemption programme, combined with health staff training and supervision, did not lead to any deterioration in the quality of drug prescriptions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".