Effectiveness of Participatory Ergonomic Interventions on Work-Related Musculoskeletal Disorders, Sick Absenteeism, and Work Performance Among Nurses: Systematic Review
Bibliographic record
Abstract
Background: Nurses face a higher risk of developing work-related musculoskeletal disorders (WMSDs) due to their primary roles in patient care. Participatory ergonomics (PE), an approach that integrates large-scale interventions performed at organizational and systems levels with small-scale interventions, is widely considered a promising approach to mitigate health problems at the workplace. However, its effectiveness in addressing WMSDs and secondary outcomes such as sickness absence and work performance among nurses is not fully understood. Objective: This systematic review assessed the effectiveness of PE interventions in preventing WMSDs and mitigating two related outcomes, sickness absence and work performance, among nurses. Methods: A literature search was performed in four electronic databases, PubMed, ScienceDirect, Scopus, and PsycNet, guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to retrieve relevant papers published between 2017 and 2023. Papers fulfilling the eligibility criteria were analyzed and subjected to quality appraisal. Results: Overall, 19 papers were included in the final analysis. Various categories of ergonomic interventions were identified, with the predominant being exercise and physical activities, health promotional activities and training, educational programs, and patient handling devices. Multicomponent interventions, especially those involving physical activities and exercise, demonstrated stronger effects in reducing the risk of WMSDs at 6 months (OR 1.64, 95% CI 1.12-4.54) and 12 months postintervention (OR 2.70, 95% CI 1.52-4.51) compared with single interventions. However, most ergonomic interventions had no statistically significant effect (P>.05) on sickness absence and work performance. More than half (n=13) of the studies demonstrated moderate to high risk of bias, reflecting the need for better quality interventions. Conclusions: Multicomponent interventions, particularly those involving physical activities and exercise, are more effective in reducing the risk of WMSDs among nurses compared with individual interventions. However, their long-term effects in addressing WMSDs, sick absenteeism, and work performance are still unclear. These gaps could be addressed by integrating organizational factors and prevention policies into existing ergonomic interventions, thereby offering opportunities to improve psychological health, job satisfaction, and work dynamics.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".