Inter-task transfer of meaningful, functional skills following a cognitive-based treatment: Results of three multiple baseline design experiments in adults with chronic stroke
Bibliographic record
Abstract
The transfer of skills learned in rehabilitation to new skills in the home has hitherto been notoriously difficult to achieve. The Cognitive Orientation to daily Occupational Performance (CO-OP) treatment approach has been associated with improved performance in people living with stroke, but the specific impact on transfer to untrained skills has not been investigated. The objective of the study was to investigate the capacity of CO-OP treatment to improve performance in both trained and untrained self-selected skills in adults living with stroke. A single case experiment with multiple baselines across skills was conducted, with two replications. The participants self-selected four skills; three were trained using CO-OP; the fourth was not. Using video recording, data points were collected at multiple baselines, during intervention, post-intervention, and at follow-up. The Performance Quality Rating Scale (PQRS) was used by an independent rater to score performances. The two-standard deviation band method was used to determine the significance of improvements. At follow-up, significant performance improvements were seen in all three single case experiments in all trained and untrained skills. A cognitive-based approach was associated with improved performance in trained and untrained skills in three adults with chronic stroke; further controlled research is warranted.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".