Street-level workers’ inadequate knowledge and application of exemption policies in Burkina Faso jeopardize the achievement of universal health coverage: evidence from a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Street-level workers play a key role in public health policies in Africa, as they are often the ones to ensure their implementation. In Burkina Faso, the State formulated two different user-fee exemption policies for indigents, one for deliveries (2007), and one for primary healthcare (2009). The objective of this study was to measure and understand the determinants of street-level workers' knowledge and application of these exemption measures. METHODS: We used cross-sectional data collected between October 2013 and March 2014. The survey targeted 1521 health workers distributed in 498 first-line centres, 18 district hospitals, 5 regional hospitals, and 11 private or other facilities across 24 districts. We used four different random effects models to identify factors associated with knowledge and application of each of the above-mentioned exemption policies. RESULTS: Only 9.2% of workers surveyed knew of the directive exempting the worst-off, and only 5% implemented it. Knowledge and application of the delivery exemption were higher, with 27% of all health workers being aware of the delivery exemption directive and 24.2% applying it. Mobile health workers were found to be consistently more likely to apply both exemptions. Health workers who were facility heads were significantly more likely to know about the indigent exemption for primary health care and to apply it. Health workers in districts with higher proportions of very poor people were significantly more likely to know about and apply the delivery exemption. Nearly 60% of respondents indicated either 5% or 10% as the percentage of people they would deem adequate to target for exemption. CONCLUSION: This quantitative study confirmed earlier qualitative results on the importance of training and informing health workers and monitoring the measures targeting equity, to ensure compliance with government directives. The local context (e.g., hierarchy, health system, interventions) and the ideas that street-level workers have about the policy instruments can influence their effective implementation. Methods for remunerating health workers and health centres also need to be adapted to ensure equity measures are applied to achieve universal healthcare.
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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 | 0.010 |
| 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 | 0.001 |
| 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".