Low back problems and possible improvements in nursing jobs
Bibliographic record
Abstract
AIM: This paper reports a study that aimed to evaluate the workload, to identify problems leading to the higher incidence rate of work-related low back injury among nurses in the orthopaedic and intensive care unit departments of the hospital, and to gather information about improvements that the nurses would like in the workplace. BACKGROUND: The literature shows that low back injuries are common among nurses, and intervention programmes are needed to address this problem. METHOD: The hospital injury records were examined in a retrospective study. In addition, a validated questionnaire was administered between January and May 2005 to 47 nurses (23 orthopaedic and 24 intensive care nurses). The questionnaire contained questions on workload, history of back injuries, problems, possible solutions and psychophysical measures of exertion. FINDINGS: The life-time incidence and point prevalence of low back pain were 65% and 30%, respectively, in orthopaedic nurses, and 58% and 25%, respectively, in the intensive care nurses. The mean weight handled was reported to be 47 +/- 30 kg by the orthopaedic nurses and 26 +/- 10 kg by the intensive care nurses. The low back scored highest for body-part discomfort - 4.6 and 4.0, respectively, on a 10-point scale. The rate of perceived job exertion was 6.7 +/- 1.8 (very strong), and 5.8 +/- 1.9 (strong) on Borg's 10-point scale. The total effort required by the job, rated on Visual Analogue Scales, was 67 +/- 14% and 68 +/- 15% of the maximum, respectively. The Borg scores and the total effort according to the Visual Analogue Scale, and the Borg scores and force effort on the Visual Analogue Scale were moderately correlated (r = 0.53, P < 0.01 for both comparisons). CONCLUSION: The methodology proposed here is practical for job evaluation and to design a participatory ergonomic intervention aiming at reducing low back injuries in nursing jobs. There are workload differences between nursing jobs. Lifting devices, biomechanical training, bigger rooms, adequate set-up and additional staff are suggested improvements.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".