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Record W2312953105 · doi:10.2182/cjot.2012.79.2.7

Administration of the Canadian Occupational Performance Measure: Effect on practice

2012· article· en· W2312953105 on OpenAlexafffundvenueabout
Heather Colquhoun, Lori Letts, Mary Law, Joy C. MacDermid, Cheryl Missiuna

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2012
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsMcMaster UniversityOttawa Hospital
FundersCanadian Institutes of Health ResearchMcMaster University
KeywordsOccupational therapyAuditMedicinePhysical therapyClinical PracticeDocumentationIntervention (counseling)MEDLINEPsychologyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: The Canadian Occupational Performance Measure (COPM) is recommended as a systematic approach to identify issues and determine client progress in occupational therapy, yet little empirical evidence is available that supports this practice. PURPOSE: To determine if COPM administration was associated with changes in eight dimensions of occupational therapy practice. METHODS: Twenty-four occupational therapists on eight geriatric rehabilitation sites completed a before-and-after study with a repeated baseline. The eight practice dimensions were assessed after three months of usual care (no COPM use) and after three months of intervention (COPM use) using chart stimulated recall (CSR) interviews and chart audit. FINDINGS: Mean practice scores for CSR interviews indicated a statistically significant practice improvement (p < .0001) across the eight dimensions, including knowledge of client perspective, clinical decision making, clinician ability to articulate outcomes, and documentation. Chart audit indicated that COPM use resulted in identifying more occupation-focused issues. IMPLICATIONS: COPM administration could improve occupational therapy practice.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.255
GPT teacher head0.502
Teacher spread0.247 · 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

Citations37
Published2012
Admission routes4
Has abstractyes

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