A critical analysis of safety performance indicators in construction
Bibliographic record
Abstract
Purpose Safety performance indicators are a major research concern globally in the construction sector, so this study aims to systematically analyse construction safety performance indicators from some top research publications from 2000 to 2019. Design/methodology/approach Systematic review was performed using Scopus search engine and relevant publications were compiled. Visual and far reaching search in all publications were performed. Final analysis was done to evaluate selected attributes. Findings The outcome of the analysis showed growing interest in research on construction safety performance indicators since 2000. From the review, 48 safety performance indicators are identified from 41 selected publications. The most reported safety performance indicators were safety climate, safety orientation, management commitment to safety, near-miss and job site audits. It was noted further that USA, Australia, Canada and China have been international locations of attention for most research on construction safety performance indicators. The 48 safety indicators are classified into six categories, namely people indicators, culture indicators, processes indicators, infrastructure indicators, metrics indicators and technology indicators Practical implications The findings identified provide researchers and practitioners a summary of the safety indicators in the construction sector through a vision to streamline future applications and increase the safety performance in the construction sector. Originality/value A safety performance indicators' list has been established for the adoption of future empirical research. The findings will make a significant contribution to current but limited knowledge on safety performance indicators in construction industry.
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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 | 0.058 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.050 | 0.029 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".