Monitoring and Evaluation Budgetary Practices on Project Service Delivery
Bibliographic record
Abstract
Successful execution of projects is critical to the development and strategic variations within an institution. However, there have been setbacks due to the deficiencies in monitoring and evaluation (M&E) practices. M&E is an important part of any project’s success story because they oversee project effectiveness, efficiency, transparency, and accountability, among other things. A background review discloses the insufficiency of M&E practices within the institution. The purpose of the study was to examine how M&E budgetary practices affect project service delivery. The study’s theoretical foundation was based on resource-based theory. A descriptive study design was employed targeting administrators, project managers, nurses, chief of centers, and data collection staff. The study focused on a target population of 140 people. The study employed a sample size of 103 respondents using Fisher’s exact formula. The researcher exploited the purposive sampling technique during the study. Data collection instruments administered were questionnaires and key informant interviews. Reliability of instruments was through internal consistency using Cronbach’s Alpha Coefficient of 0.70 or greater while the validity of these instruments was done using content, face, and constructs validity. Quantitative data from questionnaires were analyzed using descriptive statistics used including frequencies, standard deviation, mean, and percentages with the aid of a statistical package for social science (SPSS). Pearson Coefficient Correlation and Multiple regression models were used to make inferences and generalizations. The hypotheses test was graded at the 0.05 level of significance. The study findings indicated that there was a statistically strong significant positive effect of M&E budgetary practices on project service delivery (r=0.622; p<0.05). These findings indicated the alternate hypotheses was accepted and the null hypotheses was rejected. M&E budget serves as a cost and revenue indicator for project managers’ daily operational activities. M&E budget is used to provide information and support management decisions, as well as monitor and control the organization throughout the year. This study recommends that the CBCHS institute budgetary adjustments in the budgetary processes and practices. This will help to predict the future expenses, and costs and accordingly work towards the expected revenues as well as cater adequately on the allocated resources.
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 | 0.084 | 0.216 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".