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Feasibility of the Canadian Occupational Performance Measure for Routine Use

2010· article· en· W2317128843 on OpenAlexaffabout
Heather Colquhoun, Lori Letts, Mary Law, Joy C. MacDermid, Mary Edwards

Bibliographic record

VenueBritish Journal of Occupational Therapy · 2010
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsOccupational therapyOutcome (game theory)RehabilitationMedicinePerceptionPatient-reported outcomeMeasure (data warehouse)PsychologyClinical psychologyPhysical therapyNursingQuality of life (healthcare)Computer scienceData mining

Abstract

fetched live from OpenAlex

Purpose: Despite encouragement, routine outcome measurement is not standard practice in occupational therapy. This applies across most practice areas and outcome measures, including occupational therapy measures such as the Canadian Occupational Performance Measure. Barriers to using outcome measures have been proposed, but are gathered from therapists not measuring outcomes routinely. This study gathered therapists' perceptions on outcome measurement following a period of routine outcome measure use. A secondary aim was to propose a therapist-driven template for summarising outcome data routinely. Procedures: Using a process evaluation, a short answer survey was used with three occupational therapists following 5 months of Canadian Occupational Performance Measure use in older people's rehabilitation. The data were summarised descriptively, using a proposed template based on therapist feedback. Findings: The therapists perceived that the measure facilitated treatment for both therapists and clients but they experienced challenges related to client cognition and sustaining use. Template creation indicated that the therapists placed more importance on individual than group level data. Conclusion: The therapists perceived benefit in routine Canadian Occupational Performance Measure use. The instrument appears feasible for meaningful and routine use but not necessarily for sustained use. Increasing outcome measure use is complex, requiring more knowledge on barriers, expectations of value and methods of data utilisation.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.282
GPT teacher head0.484
Teacher spread0.202 · 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 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

Citations44
Published2010
Admission routes2
Has abstractyes

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