Reliability of the Persian Version of Canadian Occupational Performance Measure for Iranian Elderly Population
Bibliographic record
Abstract
Objectives: The value of the client-centered approach for treating patients with various disabilities has been increasingly acknowledged. The aim of this study was to determine the test-retest reliability of the Persian version of the Canadian Occupational Performance Measure (COPM) as an individual outcome measure among Iranian elderly population. Methods: In this cross-sectional study, 60 older clients who fulfilled the inclusion criteria were randomly selected and underwent the measurements. Based on the performing procedure of the COPM, participants were asked to identify their most important problems within activities of daily living (ADL) and then to score them according to the amount of ability and satisfaction they experience during those activities. All participants were assessed twice, with seven days interval. The correlations between data obtained from two assessments were calculated for ability and satisfaction sections separately using Pearson coefficiency. Results: Data analysis showed that there are good correlation between mean scores of two assessments in both ability (rp=0.80, P<0.05) and satisfaction (rp=0.84, P<0.05) sections. Discussion: Results obtained from this study enhance the value of the COPM as an individual outcome measure and suggest that Persian version of the COPM has adequate test-retest reliability in selected older populations.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".