The unintended consequences of combining equity measures with performance-based financing in Burkina Faso
Bibliographic record
Abstract
BACKGROUND: User fees and poor quality of care contribute to low use of healthcare services in Burkina Faso. The government implemented an innovative intervention that combines equity measures with performance-based financing (PBF). These health equity measures included a community-based selection of indigents to receive user fee exemptions and paying healthcare centres higher purchase prices for services provided to indigents. Research suggests complex interventions can trigger changes not targeted by program planners. To date, however, there is a knowledge gap regarding the unintended consequences that can emerge from combining PBF with health equity measures. Our objective is to document unintended consequences of the equity measures in this complex intervention. METHODS: We developed a conceptual framework using the diffusion of innovations theory. For the design, we conducted a multiple case study. The cases were four healthcare facilities in one district. We collected data through 93 semi-structured interviews, informal discussions, observation, as well as intervention documents. We conducted thematic analysis using a hybrid deductive-inductive approach. We also used secondary data to describe the monthly evolution of services provided to indigent and non-indigent patients before and after indigent cards were distributed. Time series graphs were used to validate some results. RESULTS: Local actors, including members of indigent selection committees and healthcare workers, re-invented elements of the PBF equity measures over which they had control to increase their relative advantage or to adapt to implementation challenges and context. Some individuals who did not meet the local conceptualization of indigents were selected to the detriment of others who did. Healthcare providers believed that distributing free medications led to financial difficulties and drug shortages, especially given the low purchase prices and long payment delays. Healthcare workers adopted measures to limit free services delivered to indigents, which led to conflicts between indigents and providers. Ultimately, selected indigents received uncertain and unequal coverage. CONCLUSIONS: The severity of unintended consequences undermined the effectiveness and equity of the intervention. If the intervention is prolonged and expanded, decision-makers and implementers will have to address these unintended consequences to reduce inequities in accessing care.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".