A Systematic Review of Occupational Health and Safety to Prevent Fall Accidents in Civil Works
Bibliographic record
Abstract
In the construction sector, accidents that harm workers' health are common and are caused by different factors, such as inadequate work area and human factors.Therefore, it is essential to comply with occupational health and safety regulations to avoid accidental falls from different heights and ensure workers' health.This study aimed to analyze the scientific production of Occupational Health and Safety related to fall accidents in the construction sector through a bibliometric analysis and a systematic review focused on identifying current knowledge, research trends, and knowledge gaps.Using databases such as Scopus and Web of Science (WoS), 1,419 documents published between 1968, and October 2024 were collected and analyzed using analytical tools such as Vosviewer and Bibliometrix.In addition, the PRISMA method was applied for a selection analysis of scientific publications using the following exclusion criteria: i) articles not focused on falls (within titles and keywords), ii) articles that are not in English, iii) articles that are not open access, obtaining 59 relevant publications that highlight innovative approaches such as deep learning, virtual reality (VR) and artificial intelligence (AI) in the field of occupational safety.The findings of this work show an increase in research on these topics, highlighting their relevance in the prevention of occupational accidents, and offer a comprehensive view of current trends, application of technologies and knowledge gaps (e.g., lack of safety training integrating advanced technologies such as AI and VR, lack of safety culture, socioeconomic factors in some countries in the application of regulations) laying the foundations for future, more in-depth research in this field.
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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.039 | 0.036 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".