A comprehensive systematic review of safety leading indicators in construction
Bibliographic record
Abstract
Safety leading indicators have gained attention as an emerging field within the construction industry. However, there is a lack of consensus regarding the fundamental aspects of leading indicators, including their definitions, effectiveness, and implementation. This study aims to extract the evolved definition of safety leading indicators, identify trends, and shifts in their context, investigate the relationship between these indicators and safety management factors, and evaluate the effectiveness of their implementation in construction projects. A total of 728 journal articles were selected using preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. These articles were analyzed to present a comprehensive overview of the body of knowledge on safety leading indicators. The analysis focused on identifying key themes, trends, and insights related to these indicators in the construction industry. The research findings emphasized the continuous development and refinement of the definition of safety leading indicators over time. Moreover, the study identified four emerging trends, revealing the evolving nature of safety management practices. Furthermore, it underscored the challenge of establishing direct links between these indicators and other safety management elements due to the intricacy of factors contributing to safety performance. Lastly, the study assessed the effectiveness of implementing safety leading indicators in construction projects, providing valuable insights on their actual impact. This study contributes to the field by providing a comprehensive review of safety leading indicators in the construction industry. This knowledge adds value by offering guidance for future research endeavors related to safety leading indicators in the 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 | 0.022 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".