Responsiveness of the Canadian Occupational Performance Measure
Bibliographic record
Abstract
This study evaluated the responsiveness of the Canadian Occupational Performance Measure (COPM), an individualized, client-centered outcome measure for the identification and evaluation of self-perceived occupational performance problems. We recruited 152 consecutive patients with various diagnoses, admitted to the outpatient clinic of two occupational therapy departments, to complete a COPM interview and three self-reported health status questionnaires on two occasions: prior to the start of occupational therapy treatment and 3 months later. The three questionnaires were the Sickness Impact Profile (SIP68), the Disability and Impact Profile (DIP), and the Impact on Participation and Autonomy (IPA). We assessed criterion responsiveness by calculating the area under the curve (AUC) for the receiver operating characteristic curve and the optimal cutoff values for the COPM scores.To determine construct responsiveness, we calculated correlations between the change in COPM scores and the change in the SIP68, DIP, and IPA scores. The AUC ranged from 0.79 to 0.85, and the optimal cut-off values for the performance scores and satisfaction scores ranged from 0.9 to 1.9.We found significant positive correlations between the COPM scores and the SIP68, DIP, and IPA scores. The capability of the COPM to detect changes in perceived occupational performance issues is supported.
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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 | 0.011 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".