Influence of Health and Safety Training, Safety Monitoring and Enforcement of Compliance on Employee Efficiency in Manufacturing Firms
Bibliographic record
Abstract
The purpose of the study is to investigate the influence of health and safety training, safety monitoring, and enforcement of compliance on employee efficiency in manufacturing firms. The research employed the quantitative approach involving a descriptive survey. A sample size of 360 respondents was randomly selected for the study. A questionnaire instrument was used in gathering primary data for the study. Confirmatory Factor Analysis (CFA) was used in providing a comprehensive validation of the measurement instrument. The required inferential statistics including normality, multicollinearity, and heteroscedasticity tests were performed and were satisfactory. Structural Equation Model (SEM) was used to estimate structural relationships between health and safety training, safety monitoring and enforcement of compliance on employee efficiency. The research results showed that health and safety training has a significant positive effect on employee efficiency with a p-value of 0.000; safety monitoring has a significant positive effect on employee efficiency with a p-value of 0.000 and enforcement of compliance has a significant positive effect on employee efficiency with a p-value of 0.000. The research brings to the fore and creates awareness on the influence of health and safety training, safety monitoring, and enforcement of compliance to safety and health standard towards enhancing workers' safety, health and welfare for improved employee efficiency. Manufacturing firms should ensure adequate health and safety training and proper safety monitoring and enforcement of compliance to safety and health standard to reduce accidents and improve employee efficiency and performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".