Assessment of training and development and labour turnover in Federal University of Technology, Minna And Ibrahim Badamasi Babangida 2010 -2020 / Sani Yusuf, S. B. Abdulkareem and H. A. Yusuf
Bibliographic record
Abstract
Training and development are assumed to have a very positive effects on workers in every organization most especially universities. They tend to be more productive whenever human capital development are undertaken as policy or strategy as this will eventually lead to productivity. In Nigeria, employee turnover posed serious fundamental challenges in many organization most especially universities. A recent estimate have shown that between 2010 to 2020, more than 4000 teaching staff from various universities leave their various institutions to look for a greener pastures in western countries such as United Kingdom, United States, Canada and Germany. How does training and development affect labour turnover in our universities? The objective of this paper is to determine the effect of training and development on turnover. The hypothesis postulated state that training and development has no significant effect on turnover. Some variables such as training needs, relevance of training and development programmes, proper placement after training, promotion, salary increase, recognition, and career growth were identified to be factors that contribute to labour turnover. The research adopted both secondary and survey research method. The population of the study was 2,010. The sample size was 305. A stratified sample techniques was adopted. The questionnaire were administered, collected and analyzed using descriptive statistical analysis and regression to test the hypothesis. The findings established that these variables has a significant and positive relationship with turnover. The study recommends that workers should be highly motivated with promotion, proper placement, and uninterrupted career growth.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".