Enhancing Efficiency and User Experience of Digital Community Health Worker Payments in Zanzibar: Implementation Report
Bibliographic record
Abstract
Background: Community health workers (CHWs) are essential for achieving universal health coverage and reaching the Sustainable Development Goals. Paying CHWs for their work increases their motivation and effectiveness, and is recommended by the World Health Organization and advocacy organizations such as the Community Health Impact Coalition. Many implementing organizations currently pay CHWs using mobile money or other digital means. However, most payment systems are designed without the involvement of CHWs. Objective: In this implementation report, we describe efforts to improve efficiency, accuracy, and user experience of the CHW payment process of the Jamii ni Afya project in Zanzibar. Methods: We applied Medic's design process to develop new functionality for the open-source Community Health Toolkit. We reviewed documentation and engaged with users to understand their needs and experiences with the current payment system. This information formed the basis of technical specifications, which were developed into a revised workflow. The workflow was iteratively tested and refined. Several steps that were managed offline, such as resolving payment discrepancies, were formalized and incorporated into the workflow. We conducted user acceptance testing to assess functionality and user experience. Unlabelled: The workflow was able to accurately translate programmatic data into payment information for each CHW and securely transmitted this information to a payment service provider. User acceptance testing revealed that CHWs felt the revised payment system provided them with more information and gave them a greater sense of control. Program staff felt the workflow would increase the efficiency and accuracy of the payment process, while simplifying the resolution of payment discrepancies. Conclusions: Engaging users in the design and optimization of digital payment systems has the potential to improve the efficiency and accuracy of digital payment systems while enhancing satisfaction among all users, contributing to improved sustainability and impact of CHW programs. Definitive conclusions will depend on evaluation of the system after implementation.
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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 | 0.056 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".