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Record W1864870252 · doi:10.1177/216507990905700904

Evaluation of Ceiling Lifts in Health Care Settings

2009· article· en· W1864870252 on OpenAlexaff
Hasanat Alamgir, Olivia Wei Li, Erin Gorman, Catherine Fast, Shicheng Yu, Catherine Kidd

Bibliographic record

VenueAAOHN Journal · 2009
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsCeiling (cloud)Ceiling effectMedicineHealth careAcute careUrinary incontinenceLift (data mining)Physical therapySurgeryEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.867
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.078
GPT teacher head0.490
Teacher spread0.412 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2009
Admission routes1
Has abstractyes

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