Cognitive Orientation to Daily Occupational Performance’s (CO-OP’s) Effectiveness in the Subacute Stroke Population
Bibliographic record
Abstract
Abstract The “Combined Cognitive-Strategy and Task-Specific Training Improve Transfer to Untrained Activities in Subacute Stroke: An Exploratory Randomized Controlled Trial” article analyzed the effect of the Cognitive Orientation to Daily Occupational Performance (CO-OP) approach against regular outpatient occupational therapy (OT) on improving participants’ occupational performance and participation post-stroke. It employed an exploratory, randomized controlled trial at two outpatient OT clinics in Canada and the United States. Outcomes were measured using the Canadian Occupational Performance Measure, the Performance Quality Rating Scale (PQRS), the Community Participation Index, the Stroke Impact Scale, and the Self-Efficacy Gauge. Twenty-six participants were evaluated. At post-intervention, CO-OP over usual care had a medium effect size for PQRS trained activities and a large effect for PQRS untrained activities. At 3-month follow-up, CO-OP over usual care had large effect sizes for PQRS trained and untrained activities and medium effect sizes for change in participation via the Community Participation Index and self-efficacy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".