Risk assessment, implementation of occupational health, safety and hygiene in small and medium manufacturing enterprises: A case study in central Vietnam
Bibliographic record
Abstract
This study aims to identify and evaluate the influence of the factors affecting the implementation of occupational health and safety of employees and employers and its impact on occupational health and safety and legalize risk assessment in small and medium-sized manufacturing enterprises in central Vietnam through the survey among 246 business representatives and data processing through the software SPSS 20 and AMOS 20. The results show that there are 3 factors affecting the implementation of occupational health, safety and hygiene. In order: (1) Safety regulations and instructions; (2) Occupational health, safety policy and (3) Occupational health and safety training. There is no relationship between the impacts on the implementation of occupational health and safety of the employer. In addition, a very interesting finding about the relationship of factors implementing occupational health and safety of employers was a positive influence on the legalization of risk assessment activities. On the basis of these results, employees, business owners and state management agencies will have grounds to offer useful solutions in risk assessment in order to better perform safety work and occupational health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".