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Record W2767690252 · doi:10.1108/jet-03-2017-0012

Which grab bar do you prefer in the bathroom?

2017· article· en· W2767690252 on OpenAlexaffabout
Ernesto Morales, Marc-Antoine Pilon, Olivier Doyle, Véronique Gauthier, Stéphanie Gamache, François Routhier, Jacqueline Rousseau

Bibliographic record

VenueJournal of Enabling Technologies · 2017
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsCentre intégré universitaire de santé et de services sociaux de la Capitale-NationaleCentre for Interdisciplinary Research in RehabilitationUniversité de MontréalUniversité Laval
Fundersnot available
KeywordsBathtubToiletBar (unit)Likert scaleTest (biology)PreferenceEngineeringPsychologyMathematicsStatisticsGeography

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to verify whether the horizontal grab bar for the toilet and the bathtub suggested by the Code du bâtiment du Québec conform to users’ preferences. Perceived effort, comfort and safety were considered. Design/methodology/approach In total, 31 adults and seniors using manual and powered wheelchairs were asked to test different grab bar configurations for both the toilet and bathtub. A questionnaire was designed to evaluate participants’ perceptions and preferences after the trials with each grab bar. Effort was measured using the ten-level Borg scale, while participants’ comfort and safety were assessed with a five-point Likert scale. Participants were finally invited to express an overall personal preference between the two grab bar used in each setup. Findings Participants showed preference for an L-shaped grab bar for the toilet, and a horizontal grab bar for the bathtub. The authors’ results differ from the recommendations of the barrier-free design standards of the province of Quebec’s construction code, which states that horizontal grab bars should be used for the toilet and bathtub. Originality/value This study suggest that despite the limited sample, there is an undeniable need for testing norms for public spaces, whenever is possible and has a direct effect on end-users, before publishing 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)0.001
metaresearch head score (Gemma)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0090.002

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 head0.138
GPT teacher head0.456
Teacher spread0.317 · 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

Citations7
Published2017
Admission routes2
Has abstractyes

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