Feasibility of the Canadian Occupational Performance Measure for Routine Use
Bibliographic record
Abstract
Purpose: Despite encouragement, routine outcome measurement is not standard practice in occupational therapy. This applies across most practice areas and outcome measures, including occupational therapy measures such as the Canadian Occupational Performance Measure. Barriers to using outcome measures have been proposed, but are gathered from therapists not measuring outcomes routinely. This study gathered therapists' perceptions on outcome measurement following a period of routine outcome measure use. A secondary aim was to propose a therapist-driven template for summarising outcome data routinely. Procedures: Using a process evaluation, a short answer survey was used with three occupational therapists following 5 months of Canadian Occupational Performance Measure use in older people's rehabilitation. The data were summarised descriptively, using a proposed template based on therapist feedback. Findings: The therapists perceived that the measure facilitated treatment for both therapists and clients but they experienced challenges related to client cognition and sustaining use. Template creation indicated that the therapists placed more importance on individual than group level data. Conclusion: The therapists perceived benefit in routine Canadian Occupational Performance Measure use. The instrument appears feasible for meaningful and routine use but not necessarily for sustained use. Increasing outcome measure use is complex, requiring more knowledge on barriers, expectations of value and methods of data utilisation.
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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 | 0.069 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".