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Record W2078908636 · doi:10.1177/000841740507200508

Clinical Report: Use of the Canadian Occupational Performance Measure in Vision Technology

2005· article· en· W2078908636 on OpenAlexaffvenueabout
Linda S. Petty, Laurie McArthur, Jutta Treviranus

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2005
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOccupational therapyAssistive technologyAgency (philosophy)AccountabilityService delivery frameworkProcess (computing)Service (business)MedicinePsychologyPhysical therapyComputer scienceBusinessHuman–computer interaction

Abstract

fetched live from OpenAlex

BACKGROUND: The Canadian Occupational Performance Measure (COPM) has gained wide acceptance in general occupational therapy research and practice, however, the use of the COPM in assistive technology assessments and outcomes is not as well documented. PURPOSE: This clinical report discusses the utility of the COPM in assistive technology, as illustrated by the assessment and follow-up of clients requiring high technology vision aids. RESULTS: The COPM makes important contributions to the outcomes of providing vision aids. The COPM ensures a needs review that incorporates all areas of occupational performance, which in turn directs the clinician to match the technology to client needs. From a clinical perspective, the quantitative follow-up data are helpful to determine clients' improvement in occupational performance as well as their satisfaction with the assistive technology. For administrative purposes, the COPM results provides accountability to the funding agency. PRACTICE IMPLICATIONS: The COPM can be readily integrated into the assessment and follow-up of assistive technology service delivery and adds value to both components of the 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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.842
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.305
GPT teacher head0.517
Teacher spread0.212 · 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.

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

Citations18
Published2005
Admission routes3
Has abstractyes

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