A Critical Study On The Effect Of Staff Training On Employees? Work Attitude.
Bibliographic record
Abstract
Training employees is an important activity for any organization. It instills workforce competence in the organization and boosts employee?s level of effectiveness in carrying out their responsibilities. There is evidence in the literature that shows that employee training increases an organization?s productivity levels and competitive advantage because it leads to greater retention and effective task completion. The study sought to provide empirical evidence of change in employees? attitude towards their jobs and employer after carrying out job training. The study was carried out at Star Times offices in Lagos Nigeria, where 20 employees (9 males, 11 females) of the company from the Sales and Marketing department were selected for the study. The company carried a massive training of her employees in the department, which provided an opportunity to check the change in their attitudes. The study showed that women (72.3, n=8) realized the greater change in their attitudes towards their jobs compared to their male counterparts (66.7%, n=6). The result (χ2= 6.492, df = 2, p=<0.5). There was a marked increase in job satisfaction (50% to 80%), teamwork (50% to 85%), feeling of competency (60% to 80%), and organizational commitment (65% to 80%). The study concludes that job training affects employees? attitude towards their jobs and employers positively.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".