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Record W206979174 · doi:10.1177/216507990205000306

Effectiveness of Installing Overhead Ceiling Lifts

2002· article· en· W206979174 on OpenAlexaff
Lisa A. Ronald, Analee Yassi, Jerry Spiegel, Robert B. Tate, Don Tait, Michelle R. Mozel

Bibliographic record

VenueAAOHN Journal · 2002
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCeiling (cloud)Poisson regressionMedicineCeiling effectUnit (ring theory)Operations managementEmergency medicineEngineeringMathematicsEnvironmental healthStructural engineering

Abstract

fetched live from OpenAlex

The effectiveness of replacing floor lifts with mechanical ceiling lifts was evaluated in the extended care unit of a British Columbia hospital. Sixty-five ceiling lifts were installed between April and August 1998. Injury data were abstracted from injury reports for all staff musculoskeletal injuries (MSI) occurring in the unit during a 3 year period prior to installation and a 1.5 year follow up period. Descriptive statistics were calculated for injuries pre- versus post-installation. Rates were calculated as number of injuries per 100,000 worked hours. Rates for three pre- and three post-installation intervals were compared using Poisson regression. The rate of MSI caused by lifting/transferring patients was significantly reduced (58% reduction, p = .011) after installation, but rates of all MSI and MSI caused by repositioning did not statistically decline (p > .05). Further follow up is necessary to determine whether or not ceiling lifts also can be effective for decreasing injuries related to repositioning patients on this unit.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.276
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations77
Published2002
Admission routes1
Has abstractyes

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