REDECA Framework Enhancing Occupational Safety and Health Through Artificial Intelligence Applications
Bibliographic record
Abstract
Objective: This paper aims to show how REDECA Reengineering Delphi and Evaluation can be integrated with Artificial Intelligence (AI) in a way to increase the influence of AI on Occupational Safety and Health (OSH) by further advancing the risk identification process, the prevention of injuries, and the compliance with safety standards.Methods: A quantitative cross-sectional study method was used through multiple regressions analysis for the relationships between AI application, risk identification, injury reduction, safety culture, and compliance. Organizational safety culture was explored further as a moderator influencing the effectiveness of AI in OSH systems.Results: AI enhances the identification and prediction of risk, resulting in a significant reduction in workplace injuries and fatalities. AI-enabled applications ensure higher adherence to safety protocols and helped in building a time-tested safety culture. In fact, organizational safety culture improves the effectiveness of AI, serving as a vital moderating factor that facilitates lasting advancements in workplace safety practices. This points to the relationship between technological innovation and organizational influences on better OSH outcomes.Novelty: This study presents an original integration of AI-driven predictive safety mechanisms through the REDECA framework, highlighting the moderating role of safety culture. This serves as a bridge between technology adoption and organizational behavior to advance workplace safety strategies.Research Implication: The findings provide a roadmap to organizations to not just invest in AI-based safety systems but also to inculcate a strong safety culture to reap the rewards of technical advances. This research sends a message to the fostering of the AI integration as a transformative approach for OSH management, which aims for the sustainable improvements in workplace safety, risk mitigation and employed well-being for the policymakers and the industry leaders.
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 | 0.022 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".