Perspectives of Consultants on Health and Safety Provisions in the Labour Act: A Study into Theory and Practicals
Bibliographic record
Abstract
The construction industry has been seen as one of the hazardous industries. This is because the industry has a poor health and safety performance record compared to other industries all over the world. The Labour Act provides that it is the duty of an employer to ensure that every worker employed by him or her works under satisfactory, safe and healthy conditions. The objective of this study is to identify how clauses in the Labour Act 651 addressing appropriate health and safety standards are used in construction sites and identify possible challenges facing the adaptation of the requirements of Health and Safety in the Labour Act. Using convenience and snowball sampling techniques, 200 questionnaires were distributed to architects, quantity surveyors, site and structural engineers. One hundred and twenty-one were retrieved representing a response rate of 61.5%. The data was analysed using descriptive statistics, and Relative Important Index ranking. The findings indicate that clauses in the Labour Act 651 addressing appropriate health and safety standards are poorly adhered to. The findings also indicate that the key challenges facing the adaptation of the Labour Act are; inadequate training, poor risk assessment, cost, reporting shortfalls ,lack of H&S professionals, inadequate H&S policies, data collection shortfalls, lack of H&S education, communication shortfalls and workers attitudes towards Health and Safety.
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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 | 0.037 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.019 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.009 |
| 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".