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Occupational Therapists’ Perceptions about the Non-Use of Recommended Assistive Technology (AT)

2011· book-chapter· en· W2489569885 on OpenAlexaff
Patricia Wielandt

Bibliographic record

VenueIGI Global eBooks · 2011
Typebook-chapter
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPerceptionIntervention (counseling)Occupational therapyMatching (statistics)PsychologyAssistive technologyProcess (computing)Applied psychologyMedical educationMedicineComputer scienceHuman–computer interactionPsychiatry

Abstract

fetched live from OpenAlex

First this chapter will present an overview about assistive technology (AT). Next factors identified in the literature and thought to influence AT use will be presented. The second part of the chapter will present the findings extracted from a larger three-phase study, which aimed to obtain occupational therapists’ perceptions about AT non-use. Drawing from their experiences therapists identified the client-, AT-, intervention-focused factors which they had found to influence use. Some of these factors were similar to those identified in the literature, with therapists offering additional perspectives about the role these issues actually played in affecting AT use. In addition, therapists highlighted other important factors, not previously identified as influential or which had received little attention. Overall results showed support for a client-centred approach during the provision of AT. Suggestions were made to incorporate the Matching Person with Technology (MPT) model into current practice to guide the AT provision process.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.766
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.120
GPT teacher head0.402
Teacher spread0.282 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations0
Published2011
Admission routes1
Has abstractyes

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