The role of participatory ergonomics in supporting the safety of healthcare workers; a systematic review
Bibliographic record
Abstract
Despite the special attention given to safety in healthcare, most of the efforts are centered around patients. This study reviews the literature to explore the use of participatory ergonomics approaches to promote the safety of healthcare workers in clinical settings and the implementation challenges faced. This review follows PRISMA guidelines and utilizes the Pico framework to search databases for peer-reviewed articles on participatory ergonomics interventions for workers’ safety. The search was conducted in April 2023. Quality assurance included the snowball method and manual searches in relevant safety and ergonomics journals. Several studies (N = 36) were included in the review. The identified safety issues addressed by participatory ergonomics are Musculoskeletal injuries (N = 14), occupational injuries (N = 8), performance in complex systems (N = 7), medication errors and management (N = 3), physical load (N = 2), and occupational stress (N = 2). Many implementation challenges were faced, such as infections, violence, burnout, staffing retention, and Covid-19-related challenges. These findings can contribute to the development of evidence-based policies, guidelines, and recommendations to support the integration of participatory ergonomics in healthcare safety programs, which can help reduce occupational hazards.
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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 | 0.035 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".