Model predicting cost benefit analysis (cba) of accident prevention on construction projects
Bibliographic record
Abstract
Health and safety issues are major concerns in the United Kingdom (UK) construction industry. Evidence suggests that research studies on construction health and safety management issues have yet to lead to a signifi cant reduction in the number of accidents. To tackle the causes of days lost through accidents and to improve health and safety performance in the construction industry, the industry needs to understand the cost benefi t analysis (CBA) of accident prevention. The paper reviewed the rate of accidents in the UK construction industry and presents a model predicting CBA of accident prevention on construction projects. A quantitative method approach was used to collect data from health and safety managers in the UK construction industry for the survey. A total of 79 contractors (small, medium and large) partici-pated in the questionnaire survey. A simple linear regression model was adopted to identify the effect of total costs of accident prevention on benefi ts of accident prevention. The result revealed that costs of accident prevention are signifi cantly associated with benefi ts of accident prevention. The model predicted that the more the contractors spend on accident prevention the more the benefi t of accident prevention they derived. This is part of a wider study to improve the management of health and safety and to propose a way forward for safer and healthier construction sites.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".