Safety analysis of patient transfers and handling tasks
Bibliographic record
Abstract
BACKGROUND: Low-back disorders are related to biomechanical demands, and nurses are among the professionals with the highest rates. Quantification of risk factors is important for safety assessment and reduction of low-back disorders. OBJECTIVE: This study aimed to quantify physical demands of frequent nursing tasks and provide evidence-based recommendations to increase low-back safety. METHODS: Thirty-six volunteer female nurses participated in a cross-sectional study of nine nursing tasks. Lumbar range of motion (ROM) and motion during nursing tasks were measured. Compression and shear forces at L5/S1, ligament strain and percentage of population without sufficient torso strength to perform 14 phases of nine nursing tasks were estimated. RESULTS: Peak flexions during trolley-to-bed, bed-to-chair and chair-to-bed transfers reached the maximum flexion ROM of the nurses. Average lumbar flexion during trolley-to-bed transfers was >50% of flexion ROM, being higher than during all other tasks. Mean (SD) compression at L5/S1 (4754 N (437 N)) and population without sufficient torso strength (37% (9%)) were highest during the pushing phase of bed-to-trolley transfers. Shear force (487 N (40 N)) and ligament strain (14% (5%)) were highest during the pulling phase of trolley-to-bed transfers. CONCLUSIONS: Nursing tasks impose high biomechanical demands on the lumbar spine. Excessive lumbar flexion and forces are critical aspects of manual transfers requiring most of the nurses' capabilities. Evidence-based recommendations to improve low-back safety in common nursing tasks were provided. Fitness to work, job modifications and training programs can now be designed and assessed based on the results.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".