Cognitive strategy use to enhance motor skill acquisition post-stroke: A critical review
Bibliographic record
Abstract
OBJECTIVE: The objective of this critical review was to examine the literature regarding the use of cognitive strategies to acquire motor skills in people who have had a stroke, to determine which strategies are in use and to compile evidence of their effectiveness. SEARCH TERMS: A computerized search of a range of databases was conducted using the following search terms: stroke, cerebrovascular accident; combined with strategy training, learning strateg*, cognitive strateg*, metacognitive strateg*, goal setting, goal planning, goal attainment, goal direct*, goal orient*, self talk, imagery, mental practice, self evaluat*, ready*, attentional focus*, problem solv*, goal management; combined with motor, mobility, activit*, skill, task, function, ADL. RESULTS: Twenty-six articles were reviewed. Seven studies investigated general cognitive strategies and 19 investigated task-specific strategies. The most commonly studied task-specific strategy was motor imagery. Findings suggest that general strategy training improves performance in both trained and untrained activities compared to traditional therapy; and that a specific motor imagery protocol can improve mobility and recovery in the affected upper extremity in people living with the chronic effects of stroke. CONCLUSION: This foundational evidence supports the further development of novel cognitive strategy-based interventions with the intention of improving long-term stroke outcomes.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".