Computer aided self-regulation learning and cognitive training improve generalization ability of patients with poststroke cognitive impairment
Bibliographic record
Abstract
Emerging studies suggest the application of self-regulation learning (SRL) to improve generalization abilities in poststroke patients. SRL has been proposed to have an added effect on computer-aided cognitive training (CACT). This study aimed to examine the efficacy of an intervention combining computer-aided SRL (CA-SRL) training and CACT for generalization abilities and cognitive function in patients with poststroke cognitive impairment (PSCI). A total of 75 patients recruited from a rehabilitation centre were randomly assigned to a CA-SRL group, demonstration learning (DL) group and traditional learning (TL) group. Finally, 72 patients were included in the analysis. Over 3 weeks, the patients in these three groups underwent CA-SRL or DL training combined with cognitive training. After the intervention, all outcomes significantly improved (P < 0.05). The CA-SRL group showed better improvements in all trained tasks among the groups, especially in "wash the dishes" and "change the bed". The results of the analysis for generalization abilities showed that CA-SRL group patients obtained the highest scores among the groups in untrained tasks. The mean changes in the Montreal Cognitive Assessment in both the CA-SRL and TL groups were significantly higher than those in the DL group (P < 0.001, P = 0.002) after adjusting for education level and Lawton Instrumental ADL Scale score. In general, the combination of CA-SRL and CACT is effective for PSCI patients and has a better effect on promoting skill generalization from cognitive gains than traditional training.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".