Monitoring and Evaluation Data Collection Practices and Performance of Livelihood Programmes: A Case of Caritas, Catholic Diocese of Meru, Kenya
Bibliographic record
Abstract
The objective of the study was to determine the influence of monitoring and evaluation data collection practices on the performance of livelihood programmes at Caritas Meru, Kenya. The target population was 465 composed of 441 smallholder farmer group leaders 21 project staff and 3 senior managers of Caritas Meru. The Sample size was 215 in clusters of 191 farmer group leaders, 21 project staff, and 3 senior managers, calculated using the Cooper and Schindler (2003) formula. Questionnaires, Key informant interviews, and Focus Group Discussions were utilized to collect data. Descriptive statistics comprises frequencies, percentages, means, and composite mean whereas Pearson correlation (r) and multiple regression analysis were used as inferential statistics. The study found that M&E data collection practices are effectively used at Caritas Meru with a composite mean of 3.98 and that the livelihood programmes had good performance with a composite mean score of 3.87. The results indicate that there was a positive correlation between monitoring and evaluation data collection and performance of livelihood programs, r (207) = .453, p < .05. The null hypothesis (H0) was thus rejected since p=0.000<0.05. The study concluded that M&E data collection practices were a significant variable influencing the performance of livelihood programs at Caritas Meru. The study recommended effective and efficient use of M&E data collection best practices to deliver, valid and reliable data to promote project performance. The study recommends a further study using a similar methodology on programmes in other sectors for the generalization of the results.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".