Why Do Nurses Have a High Incidence of Low Back Disorders, and What Can Be Done to Reduce Their Risk?
Bibliographic record
Abstract
Work-related low back disorders (WLBD) are the most frequent and costly musculoskeletal disorder seen, and are related to high working demands. Nurses are among the professionals with the highest rates of WLBD. This paper summarizes the results of research on WLBD among nurses in an acute care teaching hospital. Injury records were reviewed and a questionnaire survey was conducted of 47 nurses from jobs with the highest WLBD incidence rate. The working-life incidence rates of WLBD and point prevalence of low back pain were 65% and 30% for orthopedic nurses and 58% and 25% for intensive care unit (ICU) nurses, respectively. The mean ± standard deviation perceived job exertion on a 10-point scale was 7 ± 2 for the orthopedic nurses and 6 ± 2 for the ICU nurses. Patient transfers (orthopedic nurses) and turning and repositioning patients in bed (ICU nurses) were considered the physically most demanding and risky parts of their occupation. A functional capacity evaluation of 25 nurses and a biomechanical demand analysis of manual handling and patient transfers of 36 nurses were performed. The job simulated forces (78 ± 14% of the maximum) were higher than the preferred force levels (56 ± 21%, P < 0.01). The instantaneous compression at L5/S1 (4754 ± 437 N) and population without sufficient torso strength (37 ± 9%) were highest during the pushing phase of the bed to stretcher transfers by the orthopedic nurses. Evidence-based recommendations for modifications and training programs to reduce the risk of WLBD in nurses have been proposed. Fitness for work, job modifications, and training programs can be designed based on the results presented.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".