Continuous assessment of work activities and posture in long-term care nurses
Bibliographic record
Abstract
The high prevalence of low back injuries in nursing has prompted the use of mechanical lift assists while overall assessment of activities and postures remains limited. The purpose of this study was to chronicle trunk posture and work tasks of long-term healthcare professionals. An inclinometer monitored trunk posture for 27 workers, 20 of whom were also observed continuously throughout their shift. Patient lifts and transfers accounted for less than 4% of the shift while patient care, unloaded standing and walking and miscellaneous tasks accounted for 85%. Manual lifts and transfers occurred twice as often as mechanically assisted lifts but took only half the time. The workers had a median trunk flexion angle of 9.2 degrees , spent 25% of their time flexed beyond 30 degrees and had peak flexion angles greater than 75 degrees in many tasks. Analysis of posture throughout the entire working shift indicates that, in addition to lifts and transfers, emphasis needs to be placed on patient care and miscellaneous activities when assessing injury risk for nurses. STATEMENT OF RELEVANCE: Patient handling has been the focus in the effort to reduce back pain and injury in nursing. In addition to the use of mechanical lifts, there is a need to examine other aspects of nursing, including patient care and other ancillary tasks, which comprise the majority of the work shift and, while often unloaded, exhibit extreme postures that may also lead to injury.
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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) | 0.001 | 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".