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Record W4392825521 · doi:10.1177/23337214241237119

Efficacy of Installation of Temporary Bathing Transfer Aids by Older Adults

2024· article· en· W4392825521 on OpenAlexafffund
M E Rand, James Pelchat, Iris C. Levine, Roger E. Montgomery, Rebecca Greene, Emily C. King, Steven Pong, Alison C. Novak

Bibliographic record

VenueGerontology and Geriatric Medicine · 2024
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity Health NetworkCarleton UniversityUniversity of Toronto
FundersCanadian Institutes of Health ResearchOntario Ministry of Research, Innovation and Science
KeywordsBathingSuctionEnvironmental scienceEngineeringMedicineMechanical engineering

Abstract

fetched live from OpenAlex

Grab bars facilitate bathing and reduce the risk of falls during bathing. Suction cup handholds and rim-mounted tub rails are an alternative to grab bars. The objective of this study was to determine whether older adults could install handholds and tub rails effectively to support bathing transfers. Participants installed rim-mounted tub rails and suction cup handholds in a simulated bathroom environment. Installation location and mechanical loading performance were evaluated. Participant perceptions during device installation and a bathing transfer were characterized. While 85% of suction cup handholds met loading requirements, more than half of participants installed the suction cup handhold in an unexpected location based on existing guidance documents. No rim-mounted tub rails were successfully installed. Participants were confident that the devices had been installed effectively. Suction cup handholds and rim mounted tub rails are easy to install, but clients may need additional guidance regarding where, and how to install them.

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)0.003
metaresearch head score (Gemma)0.014
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 score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.342
Teacher spread0.321 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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