EFFECTS OF HUMAN RESOURCE MANAGEMENT PRACTICES ON EMPLOYEE RETENTION IN PRIVATE HOSPITALS IN KIAMBU COUNTY, KENYA
Bibliographic record
Abstract
Purpose: The general objective of the study was to establish the human resource practices affecting employee retention in selected private hospitals in Kiambu County, Kenya. The specific objectives were: to examine the influence of career development, work life balance, work environment and compensation strategies on retention of employees in selected private hospitals in Kiambu. Methodology: Descriptive survey was employed. The study focused on the level 3 and 4 private hospitals, which are 7 within the County. This study targeted 340 employees of level 3 and 4 private hospitals in Kiambu County. The study used purposive sampling and sampled medical workers of the private hospitals in Kiambu County who were 120. The medical workers who included the doctors, nurses and clinical officers were sampled since they have knowledge on human resource practices and employee retention at the private hospitals. Findings: Findings displayed that 89 of the questionnaires we filled properly and returned. The results showed that career development, work life balance, compensation strategies and work environment had a positive and significant effect on employee retention in private hospitals. Unique Contribution to Practice and Policy: Private hospitals that do not have a coaching and mentorship programs for their employees should ensure they introduce them since they enhance employee retention. The investigation also endorses that the private hospitals should inspire active cooperation across different parts of the organization. They should also ensure that there is a career path of staffs in their firm. Since career growth and development are an integral part of every individual's career; more opportunities should be created for employees of private hospitals. In addition, private hospitals should have a clear work schedule for their employees. In addition, private hospitals should ensure they have enough leave days for their workers.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".