Pay-for-performance reduces bypassing of health facilities: Evidence from Tanzania
Bibliographic record
Abstract
Many patients and expectant mothers in low-income countries bypass local health facilities in search of better-quality services. This study examines the impact of a payment-for-performance (P4P) scheme on bypassing practices among expectant women in Tanzania. We expect the P4P intervention to reduce incidences of bypassing by improving the quality of services in local health facilities, thereby reducing the incentive to migrate. We used a difference-in-difference regression model to assess the impact of P4P on bypassing after one year and after three years. In addition, we implemented a machine learning approach to identify factors that predict bypassing. Overall, 38% of women bypassed their local health service provider to deliver in another facility. Our analysis shows that the P4P scheme significantly reduced bypassing. On average, P4P reduced bypassing in the study area by 17% (8 percentage points) over three years. We also identified two main predictors of bypassing - facility type and the distance to the closest hospital. Women are more likely to bypass if their local facility is a dispensary instead of a hospital or a health center. Women are less likely to bypass if they live close to a hospital.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".