A Performance-Based Incentives System for Village Health Workers in Kisoro, Uganda
Bibliographic record
Abstract
BACKGROUND: Village health worker (VHW) programs in Uganda have achieved limited success, due in part to a reliance on volunteerism and a lack of standardized incentive mechanisms. However, how to best incentivize VHWs remains unclear. Doctors for Global Health developed a performance-based incentives (PBI) system to pay its VHWs in Kisoro, Uganda, based on performance of tasks or achievement of targets. OBJECTIVES: 1. To describe the development of a PBI system used to compensate VHWs. 2. To report cost and health services delivery outcomes under a PBI system. 3. To provide qualitative analysis on the successes and challenges of PBI. METHODS: Internal organization records from May 2016 to April 2017 were retrospectively reviewed. The results of descriptive and analytic statistics were reported. Qualitative analysis was performed by the authors. FINDINGS: In one year, 42 VHWs performed 23,703 remunerable health actions, such as providing care of minor ailments and chronic disease. VHWs earned on average $237. The total cost to maintain the program was $29,844, or $0.72 per villager. There was 0% VHW attrition. Strengths of PBI included flexibility, accountability, higher VHW earnings, and improved monitoring and evaluation. CONCLUSIONS: PBI is a feasible and sustainable model of compensating VHWs. At a time where VHW programs are sorely needed to address limitations in healthcare resources, yet are facing challenges with workforce compensation, PBI may serve as a model for others in Uganda and around the world.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".