The Role of Routine Programme for Monitoring and Evaluation on Sub-national Water Services in Kenya
Bibliographic record
Abstract
Routine programme for monitoring and evaluation involves data gathering, analysis and reporting to ensure progress and ultimately achievement of project goals. This study determined the influence of routine programme for monitoring and evaluation on sub-national water services in Nyamira South Sub County, Kenya. A mixed method, cross-sectional and correlational design was used. Study findings were generated using quantitative interviews from a total of 480 household heads; and staff from the local water services company. Qualitative information was generated from discussions with 40 village elders. The study established that provision of water services in the study area was not sustainable, with only about 23 percent of households accessing water services. The study findings also revealed poor monitoring of the water services. However, the findings demonstrated a linear, positive, and significant association between routine programme for monitoring and evaluation and sustainability of water services. The Pearson correlation coefficient was 0.724 and p-values were 0.000 for both correlation and regression analyses. The findings support the strengthening of routine programme for monitoring and evaluation as a way of reinforcing the long-term management of water resources at sub-national level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".