The role of participatory ergonomics in supporting the safety of healthcare workers; a systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it