The feasibility of meta-cognitive strategy training in acute inpatient stroke rehabilitation: Case report
Bibliographic record
Abstract
Meta-cognitive strategy training may be used to augment inpatient rehabilitation to promote active engagement and subsequent benefit for individuals with cognitive impairments after stroke. We examined the feasibility of administering a form of meta-cognitive strategy training, Cognitive Orientation to daily Occupational Performance (CO-OP), during inpatient rehabilitation. We trained an individual with cognitive impairments after right hemisphere stroke to identify performance problems, set self-selected goals, develop plans to address goals, and evaluate performance improvements. To assess feasibility, we examined the number of meta-cognitive training sessions attended, the number of self-selected goals, and changes in goal-related performance. We also examined changes in rehabilitation engagement and disability. The participant used the meta-cognitive strategy to set eight goals addressing physically oriented, instrumental, and work-related activities. Mean improvement in Canadian Occupational Performance Measure Performance Scale scores was 6.1. Pittsburgh Rehabilitation Participation Scale scores (measuring rehabilitation engagement) improved from 3.2 at admission to 4.9 at discharge. Functional Independence Measure scores (measuring disability) improved from 68 at admission, to 97 at discharge. Performance Assessment of Self-Care Skills scores improved from 1.1 at admission to 2.9 at discharge. The results indicate that meta-cognitive strategy training was feasible during inpatient rehabilitation and warrants further evaluation to determine its effectiveness.
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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 | 0.152 | 0.380 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.020 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".