Evaluation of Ceiling Lifts in Health Care Settings
Bibliographic record
Abstract
Ceiling lifts have been introduced into health care settings to reduce manual patient lifting and thus occupational injuries. Although growing evidence supports the effectiveness of ceiling lifts, a paucity of research links indicators, such as quality of patient care or patient perceptions, to the use of these transfer devices. This study explored the relationship between ceiling lift coverage rates and measures of patient care quality (e.g., incidence of facility-acquired pressure ulcers, falls, urinary infections, urinary incontinence, and assaults [patient to staff] in acute and long-term care facilities), as well as patient perceptions of satisfaction with care received while using ceiling lifts in a complex care facility. Qualitative semi-structured interviews were used to generate data. A significant inverse relationship was found between pressure ulcer rates and ceiling lift coverage; however, this effect was attenuated by year. No significant relationships existed between ceiling lift coverage and patient outcome indicators after adding the "year" variable to the model. Patients generally approved of the use of ceiling lifts and recognized many of the benefits. Ceiling lifts are not detrimental to the quality of care received by patients, and patients prefer being transferred by ceiling lifts. The relationship between ceiling lift coverage and pressure ulcer rates warrants further investigation.
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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 | 0.020 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".