An Occupation and Participation Approach to Reading Intervention (OPARI) part II: Pilot clinical application
Bibliographic record
Abstract
The Occupation and Participation Approach to Reading Intervention (OPARI) is an intervention approach for children with reading difficulties that emphasizes reading as an important occupation of children. Part I presented the theoretical basis of the OPARI. Part II describes a pilot clinical application of the OPARI. Guided by Schkade and Schultz’s Occupational Adaptation model (OA; 2003) and blended with principles from the Cognitive Orientation to Daily Occupational Performance treatment approach (CO-OP; Polatajko & Mandich, 2004) the essential elements of the OPARI were actualized in an 8-week intervention program. The program included small group and individual therapy sessions twice a week and therapist-teacher-parent collaborations. Five children participated and were evaluated using three standardized assessments of reading, the Canadian Occupational Performance Measure (COPM), and a descriptive measure of reading participation. Qualitative data from parent reading log journals, teacher communications, and classroom observations were also gathered. Results indicated nonsignificant increases in reading scores, significant increases in perceived performance and satisfaction with reading participation as measured by the COPM, and descriptive increases in children’s participation, mastery, and frequency in reading.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".