Determinants of rural hand-pump functionality through maintenance provision in the Central African Republic
Bibliographic record
Abstract
While preventive maintenance services have emerged as promising interventions to improve the continuity of water service delivery, the operational and contextual requirements for sustained functionality within maintenance models are not well understood. This paper uses data analysis to better understand factors influencing the success of rural water service delivery within the circuit rider maintenance model in fragile contexts. Incorporating operational data from a large scale circuit rider hand-pump maintenance program in the Central African Republic, mixed-effect logistic regression models were used to identify determinants of water point functionality and payment compliance. Models were informed by data from over 16,000 maintenance visits across nine years. Faster response time, proximity to urban centers, and proximity to other hand-pumps emerged as significant factors for improving water point functionality, while proximity to maintenance program headquarters, pump functionality, and frequency of maintenance visits significantly influenced payment compliance. The observed high functionality rates of hand-pumps serviced by the maintenance program indicates the potential benefits of professionalized maintenance through the circuit rider model at promoting water system reliability in fragile contexts. Despite adaptability and resilience in implementation of the circuit rider model, insecurity and conflict remain barriers to sustaining service delivery in the Central African Republic.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".