How Do Human Resource Management Practices Predict Employee Turnover Intentions: An Empirical Survey of Teacher Training Colleges in Kenya
Bibliographic record
Abstract
This study sought to establish how Human Resource Management practices predict tutor turnover intentions in primary Teacher Training colleges (PTTCs) in Kenya. The objectives of the study were: to establish the influence of Training, Compensation, Career development and Performance management on tutor turnover intentions in PTTCs in Kenya. The scope of the study was the Nairobi Metropolitan region. Multi stage sampling was used to obtain a sample size of 152 respondents where the actual response rate was 74.3%. The findings of the study showed that training, compensation, career development and performance management were poorly practiced and that they significantly and negatively predict tutor turnover intentions in PTTCs as they collectively accounted for 28% variation in the experienced turnover intentions among the tutors. The findings raise both theoretical and practical implications for underpinning HRM practice, behavioral science theories and personnel administrative responsibilities to college principals respectively. The study calls on future research to consider the contingent effects of the tutors' demographic characteristics and the contextual factors surrounding HRM Practice in the Colleges.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".