Canadian Occupational Performance Measure the state of the art – a review
Bibliographic record
Abstract
BACKGROUND: Occupational therapists aim to deliver interventions to enhance clients' occupational performance and document the outcomes of clients' experience. The Canadian Occupational Performance Measure (COPM) is a measurement tool designed for such documentation, and a recognised measurement used worldwide. However, the authors' research and teaching experiences have revealed that there are often misinterpretations and uncertainties administering the COPM. Therefore, the aim of this paper was to present information to answer these issues. METHODS: Based on a state-of-the-art review, 856 papers were identified on PubMed, of which 36 were included together with the first author's work, to summarise relevant answers. RESULTS: Answers to the four key issues are unfolded to address the misinterpretations and uncertainties regarding: (1) the core content of the COPM, focussing on being client-centred; (2) understanding the term occupational performance; (3) reflecting on the psycho-metric status on the COPM, what it provides; (4) reflecting on necessary considerations when administering the measurement, understanding the measurement's five steps to ensure a valid and reliable use. CONCLUSION: The valid, reliable, and applicable use of the COPM can be enhanced through therapists' commitments to and understanding of these four issues. Based on this, guidelines on how to best administer the COPM are provided.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.021 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".