The Canadian Occupational Performance Measure as an outcome measure and team tool in a day treatment programme
Bibliographic record
Abstract
PURPOSE: To investigate the usefulness of the Canadian Occupational Performance Measure (COPM) in a day treatment programme for clients with rheumatoid arthritis. METHOD: The study was conducted in two parts. In the first part rehabilitation without changes in the programme was performed (n = 16). After that the COPM was introduced to all team members. In part two the COPM was used (n = 40). Clients' experiences of participation in the process were studied via a structured interview 2 - 4 weeks after discharge in both parts. Qualitative interviews were conducted with team members before part one and after completion of part two. RESULTS: Staff expressed that the COPM improved client participation in the rehabilitation process. Goals were formulated distinctly, and focused on activity and performance rather than function. Team conferences were focused on the client's needs. Outcome was considered clear and evident to the client. The changes in client routines demands thorough introduction, support and involvement, and takes time. Involvement and motivation for changing practice were difficult to obtain, this could be a result of a large staff turnover during the data collection period. CONCLUSIONS: The COPM should be seen as an aid to ensuring client participation in the goal formulation process, and facilitating treatment planning and evaluation of outcome.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".