Musculoskeletal disorder: Risk factors and coping strategies among nurses
Bibliographic record
Abstract
Background: It is established that nurses suffer from varying degrees of Musculoskeletal Disorders (MSD) at different regions of their body, which results in frequent loss of work days. Aim of study is to identify the risk factors for developing musculoskeletal disorder and to determine the coping strategies to reduce their frequency.Methods: This study was conducted in the Outpatient Departments (OPDs), intensive care units of University Hospital and also from the nursing schoolof the Faculty of Nursing, Alexandria, Egypt.Results: A high proportion of nurses reported MSD (99.0%) during the last year. Also during their whole careers at one or the other body regions, with the shoulder (97.0%) and Neck (95.0%) being the most commonly affected. Nurses with more than two pregnancies and usage of computer for more than two years were those with the most perceived risk factors for MSD. The usage of different part of body by the nurses as a coping mechanism during the nursing procedures (34.0%) and change of posture (30.0%) were the top two statistically significant coping strategies.Conclusions: The study confirms very high prevalence of MSD among the nursing staff and it was prominent at some specific body parts, of which neck and shoulder were the most affected.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".