Occupational safety in the construction industry
Bibliographic record
Abstract
BACKGROUND: The paper is a research review focusing on occupational safety in the construction industry. OBJECTIVE: The purpose is to present research that highlights the areas of occupational safety and risks and to identify areas where research is lacking. METHODS: 146 articles from scientific journals, mainly covering the construction industry in Europe, Canada, USA, Australia and Japan have been studied. The findings are presented under 11 categories: accident statistics; individual factors; legislation and regulations; ethical considerations; risk management; leadership, management, organization; competence; safety design; cost-benefit calculations; programs and models; and technical solutions. RESULTS: The research is dominated by initiatives from researchers and government authorities, while the construction industry only appears as the object for the research. There is a scarcity of research on integrated systems encompassing subcontractors, as well as a lack of research with sociological perspectives on accidents. Furthermore, only a few studies have applied a gender perspective on safety in construction, i.e. there is a need of further research in this particular area. CONCLUSIONS: A range of initiatives have been taken to increase safety in the construction industry and the initiatives are mainly reported to be successful. There are some cultural differences, but basically researchers present similar results regardless of country.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".