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Record W3157555042 · doi:10.3233/978-1-61499-304-9-559

Electronic Mobility Aid Devices for Deafblind Persons: Outcome Assessment

2013· book-chapter· en· W3157555042 on OpenAlexaboutno aff
Routhier Fran ccedil ois, Martel Val eacute rie, M Ve, Dumont Fr eacute d eacute ric, C ocirc t eacute Lise, Cloutier Danielle

Bibliographic record

VenueIOS Press eBooks · 2013
Typebook-chapter
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsnot available
Fundersnot available
KeywordsOutcome (game theory)PsychologyPhysical medicine and rehabilitationMedicineMathematics

Abstract

fetched live from OpenAlex

Given the lack of studies using standardized and objective measures on the effect of electronic systems that helps obstacle detection or orientation, the purpose of this article is to present a single-subject's case study of 4 users aged between 50 and 70 years old followed in a deafblindness program in a rehabilitation center. The Canadian measure of occupational performance suggests that the performance and the satisfaction are higher following the use of the Miniguide and the Breeze, two commercial electronic mobility aid devices, in four types of occupations (functional mobility, active leisure, community role, and socialization). The training was completed in 4 to 10 sessions (5 to 14 hours in total). The Québec User Evaluation of Satisfaction with Assistive Technology reveals high satisfaction except for one participant regarding 4 of 8 items. Finally, a follow-up interview three months after training was done to evaluate the used features, the frequency of utilization, problems, safety and the impact on functional independence.

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)0.005
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 score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.106
GPT teacher head0.388
Teacher spread0.281 · 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

Citations3
Published2013
Admission routes1
Has abstractyes

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Same venueIOS Press eBooksSame topicHearing Impairment and CommunicationFrench-language works237,207