Appraisal of the Challenges to Ensuring Occupational Health and Safety Compliance within the Nigerian Construction Industry
Bibliographic record
Abstract
The construction industry is known for the high number of accidents occurring within the industry. This is due to the hazardous working processes which have led to severe injuries, disabilities and fatalities. Towards reducing this hazards numerous health and safety regulations have been provided by construction firms in Nigeria. Despite the provision of the regulations hazards and accidents is still experienced on construction sites. Thus, this study appraised the challenges of ensuring compliance with health and safety regulations by construction workers in the Nigeria construction industry. Data were obtained from safety personnel and construction professionals using questionnaires through a convenience sampling method. One hundred and thirty-eight were used for the analysis out of one hundred and sixty-eight that was distributed to the respondents. The questionnaire was analysed using SPSS V 24 adopting Factor analysis and mean item score. The findings clearly show that construction workers compliance to health and safety requirements is below average while the factor analysis shows inadequate safety equipment, low awareness to occupational health and poor compliance to health and safety requirements are the major challenges hindering the compliance rate. The study recommends that implementation of the use of innovative measures and hi-tech devices such as radio frequency identification for effective monitoring of construction workers. It also recommends the involvement of construction workers when making the health and safety policies. This study contributes towards improving the occupational safety experienced on construction sites within the country.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".