Facing the Challenge of Patient Transfers: Using Ceiling Lifts in Healthcare Facilities
Bibliographic record
Abstract
OBJECTIVE: The objective was to review the literature on the use of ceiling lifts to perform patient transfers in healthcare settings. BACKGROUND: Manual patient transfers present a high risk of injury. Ceiling lifts are increasingly used in healthcare facilities. Despite this, little is known about the effects of this new technology. METHODS: Research and review articles were searched on five databases using specific key words and phrases. Literature citations in the articles and gray literature (e.g., technical reports, conference proceedings, magazine articles, Web sites) were also evaluated when relevant for this review. Experts in this area were contacted regarding information on the topic, potential literature, and for their suggestions. RESULTS: Few studies evaluated the use of ceiling lifts in healthcare. The studies available and the experiences of the experts contacted support the use of ceiling lifts. The musculoskeletal safety of healthcare workers and patients can be improved by the use of ceiling lifts. Having lifts available, organizing the workflow, and reducing the steps required during transfers and handling tasks can significantly lessen the risk of musculoskeletal injuries. CONCLUSIONS: Evidence supports the installation of ceiling lifts in patient rooms and recommends their use in bathrooms. However, additional studies are needed because the use of ceiling lifts in healthcare is relatively new.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".