Canadian Occupational Performance Measure: Benefits and Limitations Highlighted Using the Delphi Method and Principal Component Analysis
Bibliographic record
Abstract
Introduction: The objective of this study was to establish a baseline of current use in practice of the Canadian Occupational Performance Measure (COPM) by consulting 33 expert French occupational therapists, who trained in this method between 2012 and 2017 and use of the COPM with their clients. The areas of health intervention are pediatrics, psychiatry, neurology, and geriatrics. An email invitation to participate in the research was therefore sent to 113 occupational therapists. We received 33 responses. Methods: A novel mixed method study combined a Delphi method with a lexical analysis of experts' responses and principal component analysis (PCA). Results: The COPM was clearly identified as a tool that supports occupational therapy clinical reasoning, facilitates team decision-making for care pathways, and enables people with disabilities and health conditions to make decisions for their care. Conclusion: The Delphi study revealed that the COPM appears to be well adapted to French culture and should be widely incorporated into preregistration training.
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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 | 0.074 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".