The unintended consequences of community verifications for performance-based financing in Burkina Faso
Bibliographic record
Abstract
Performance-based financing (PBF) is being widely implemented to improve healthcare services in Africa. An essential component of PBF involves conducting community verifications, wherein investigators from local associations attempt to trace samples of patients. Community surveys are administered to patients to verify whether healthcare workers reported fictitious services to increase their revenue. At the same time, client satisfaction surveys are administered to assess whether patients are satisfied with the services received. Although some global health actors are concerned that PBF can trigger unintended consequences, this topic remains neglected. The objective of this study was to document the unintended consequences of community verification. Guided by the diffusion of innovations theory, we conducted a multiple case study. The cases were the catchment areas of seven healthcare facilities in Burkina Faso. Data were collected between January 2016 and May 2016 using non-participant observation, 92 semi-structured interviews, and informal discussions. Participants included a wide range of stakeholders, such as community verifiers, investigators, patients, and healthcare providers. Data were coded using QDA Miner, and thematic analysis was conducted. Healthcare workers did not significantly disturb or try to influence community verifiers during patient selection for community verifications. Unintended consequences included stakeholders' dissatisfaction regarding compensation modalities, work overload for community verifiers, and falsification of verification data by investigators. Community verifications led to loss of patient confidentiality as well as fears and apprehensions, although some patients were pleased to share their views regarding healthcare services. Community verifications also triggered marital issues, resulting in conflicts with, or interference from, husbands. The numerous challenges associated with locating patients in their communities led stakeholders to question the validity and utility of the results. These unintended consequences could jeopardize the overall effectiveness of community verifications. Attention should be paid to these unintended consequences to inform effective implementation and refine future interventions.
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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 | 0.019 | 0.039 |
| 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 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".