Instructions for Administering the Canadian Occupational Performance Measure with Children Themselves
Bibliographic record
Abstract
AIMS: The Canadian Occupational Performance Measure (COPM) can be used to support children to clarify their needs themselves. However, for pediatric occupational therapists it is not sufficiently clear how to effectively use the COPM with children from 8 years of age.This study aimed to formulate specific instructions for using the COPM with children themselves, based on the experience of children, parents, and occupational therapists. In addition, professional consensus on the instructions was reached. METHODS: A multi-stage approach was used to develop the instructions. Triangulation of methods was used to gather knowledge of how the COPM with children themselves is performed in daily practice: interviews with 23 children, questionnaires completed by 30 parents, interviews with 13 therapists, and 10 video recordings of COPM administration. Specific instructions were derived from this knowledge and consensus for these instructions was reached by Delphi method. RESULTS: The data were analyzed and resulted in 40 specific instructions. Consensus of at least 80% amongst 10 occupational therapists, who regularly use the COPM with children, was achieved on each instruction. CONCLUSION: There is consensus on 40 specific instructions for administering the COPM with children. Following these instructions might help children to formulate their own goals for intervention.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".