A systematic literature review on occupational accident factors in the rail construction industry: lessons learned from a quarter-century of studies globally
Bibliographic record
Abstract
The rail construction industry is notable for its large scale, substantial investment, extensive stakeholders involvement, long construction period, and intricate operation and technology. This industry is among the most dangerous due to the highest number of occupational accident cases worldwide. Therefore, it is crucial to analyse and identify the existing literature on occupational accident factors in rail construction. To address the research aim, the study identified the factors that contribute to occupational accidents using systematic review methodology. This systematic literature review adheres to the rigorous Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Relevant publications from the past 25 years were retrieved from Scopus, Web of Science (WoS), and Science Direct electronic databases. Through a meticulous review of 43 selected publications, five accident factor themes were discovered: worker, workplace, materials and equipment, organizational, and environmental influences. The detailed analysis of these themes has led to the identification of 19 specific sub-factors within these categories, providing a granular understanding of the intricate elements contributing to accidents. This study offers a foundational understanding of accident factors in the rail construction industry, paving the way for targeted OSH interventions aimed at preventing occupational accidents in the future.
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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 | 0.020 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".