Effectiveness of overhead lifting devices in reducing the risk of injury to care staff in extended care facilities
Bibliographic record
Abstract
Patient and/or resident handling is a major cause of injury to healthcare workers. The effectiveness of an overhead ceiling lift programme at mitigating the risk of injury from resident handling was evaluated by comparing injury data and staff perceptions before and after implementation of the programme, and by comparison with a similar unit that did not implement an overhead ceiling lift programme. A questionnaire was used to assess perceived risk of injury and discomfort, preferred resident handling methods, frequency of performing designated resident handling tasks, perceived physical demands, work organization, and staff satisfaction. Staff preferred overhead ceiling lifts to other methods of transfer (manual or floor lifts) when lifting or transferring residents. A significant reduction was observed in the perceived risk of injury and discomfort to the neck, shoulders, back, hands, and arms of care staff. Compensation costs due to lifting and transferring tasks were reduced by 68% for the intervention unit and increased by 68% for the comparison unit. Overhead ceiling lifts were not beneficial in reducing the perceived risk of injury, pain or discomfort, or compensation costs when used to reposition residents. The study demonstrated an overall cost-savings associated with the installation of the overhead lifts, and highlighted areas for further improvement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".