Performance Management Practices and Productivity of Employees in the Ministry of Interior and Coordination of National Government, Kenya
Bibliographic record
Abstract
Purpose: The purpose of the study was to examine the influence of performance management practices on productivity of employees in the Ministry of Interior and Coordination of National Government, Kenya. Methodology: This study used descriptive research design to collect both qualitative and quantitative data. The target population of this study was 350 officers drawn from various departments. The study used simple random sampling techniques to select from the list of departments, directorates and divisions to be included in the study. This study adopted Yamane (1967) sampling formula to calculate the sample size which provided the number of responses that should to be obtained. Therefore, the sample size for this study was 187 employees. The respondents for this study constituted heads of departments in the Ministry. Findings: The study found that performance management practices significantly affect employee productivity in the Ministry of Interior and Coordination of National Government in Kenya. Unique Contribution to Theory, Policy and Practices: Based on the study findings, it was recommended that companies should encompass their training in all their activities in order to have competitive advantage. In addition, supervisors should oversee whether the work has been done appropriately in terms of procedures as it increases employee productivity. Furthermore, it was recommended that managers should attempt to minimize stress in organizations. The goals set by companies must be able to be broken into manageable simple actions achievable in the short term with the main focus on measurable, tangible results for each quarter.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".