Proposal of a safety maturity framework in construction: Implementing leading indicators for proactive safety management
Bibliographic record
Abstract
The construction industry remains among the most hazardous sectors globally, despite significant advancements in regulatory standards over the past decades. Traditional safety management practices often rely on lagging indicators which fail to prevent future incidents or foster a proactive safety culture. Safety leading indicators, in contrast, offer a forward-looking approach by identifying risks before incidents occur. However, the effective integration of these indicators remains underexplored, with a lack of structured frameworks to guide their implementation. This research addresses this gap by developing a Safety Maturity Framework (SMF) specifically designed for construction organizations. The SMF provides a systematic model for assessing and enhancing safety performance through five progressive stages, moving from basic compliance to advanced, data-driven practices focused on continuous improvement. Building on and extending prior maturity models that emphasize cultural evolution, the SMF explicitly incorporates leading indicators as operational benchmarks at each stage of maturity, linking organizational culture with measurable safety practices. A rigorous methodology was employed, beginning with a systematic literature review to extract safety leading indicators. This was followed by semi-structured interviews with safety professionals. Thematic and content analysis revealed key patterns, including the critical role of leadership commitment, organizational learning, and workforce engagement in embedding leading indicators into day-to-day operations. Key findings also underscored the interdependent nature of leading indicators and safety culture, demonstrating how factors like leadership and culture interact dynamically to drive safety performance. The SMF serves as a practical tool for construction firms to transition from reactive safety measures to a proactive safety culture, enhancing risk anticipation, operational efficiency, and overall sustainability. The research contributes theoretically by integrating qualitative and quantitative insights into a cohesive framework and practically by providing actionable guidance for industry professionals.
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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 | 0.048 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".