Relation between body mechanics performance and nurses’ exposure of work place risk factors on the low back pain prevalence
Bibliographic record
Abstract
Background and objective: Low back pain (LBP) is a serious medical problem and considered as the most common leading causes of disability. Aim of the study: To measure the relation between body mechanics performance and nurses’ exposure of work place risk factors on the low back pain prevalence.Methods: Subject and method: Correlation design was used. A convenient sample of one hundred (n = 100) nurses both male and female working in Minia university hospital were approached to participate. Setting: The current study was performed at Minia university Hospital in Intensive Care Unit, Coronary Care Unit, Medical Care Unit, Stroke Care Unit, operation room and surgical department.Results: The majority of nurses don’t use body mechanics when turning, moving, lifting, and transferring the patients and 88% of them had pain in lumber region.Conclusions and recommendation: The nurses’ working in Minia university hospital suffering from high prevalence of LBP. The LBP complication is mainly related to exposure to many risk factors such as obesity, lack of knowledge and practice regard to body mechanics. Educational programs among nurses about body mechanics when handling and lifting the patient have important role in decrease exposure to LBP.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".