Information bias in the Canadian occupational performance measure: A qualitative study
Bibliographic record
Abstract
Purpose: The Canadian Occupational Performance Measure (COPM) is designed to measure outcomes from client-centred occupational therapy. We explored clients' views and experiences of participating in an initial COPM assessment in order to examine the potential information bias which may influence COPM scores. Material and methods: We used qualitative methods to analyse semi-structured interviews (qualitative thematic analysis) on clients at a typical Japanese rehabilitation hospital, to examine the potential information bias affecting their scores in their initial COPM assessment. Results: 19 of 20 clients (13 men; 7 women, aged 19-84 years) demonstrated potential sources of information bias in their COPM scores. We identified 15 sources of information bias, grouped into three domains: (1) bias during the selection of occupational areas (Misunderstanding client-centeredness, Misunderstanding meaningfulness, Misunderstanding occupation, and Composite occupations), (2) bias during the scoring of performance and satisfaction (Imaginary scores, Confusing scores of performance or satisfaction with importance, Ambiguous scores, Hopeful scores, Target scores, Emotional scores, Considerate scores and Humbleness scores) and (3) bias interfering future scoring (Changes in selected occupation, Forgotten scores, Ceiling effects). Conclusion: This study identifies potential sources of bias affecting COPM scores, and taking account of this result would facilitate better collaboration with clients through COPM.
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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 | 0.046 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.025 | 0.019 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".