Technology-Enabled Cognitive Strategy Intervention for Secondary Stroke Prevention: A Feasibility Study
Bibliographic record
Abstract
BACKGROUND: People with post-stroke cognitive impairment (PSCI) are at increased risk of recurrent stroke, dementia, and accelerated cognitive decline. OBJECTIVE: To examine the feasibility, safety, acceptability, and suitability of a virtually-delivered vascular risk reduction intervention that integrates tailored cognitive strategy training for people with executive function (EF) impairments post-stroke. METHODS: This case series included eight participants who completed up to ten virtual sessions focused on vascular risk reduction and metacognitive strategy training. Sessions averaged 40 min over a 4-5-week period. RESULTS: The intervention was found to be feasible, safe, and acceptable. The recruitment rate was 66.7%, and the retention rate was 87.5% (7 of 8 completed the training). No serious adverse events were reported. Most participants demonstrated improvements on the Canadian Occupational Performance Measure (COPM), with mean performance and satisfaction change scores of 1.22 ± 0.87 and 1.18 ± 0.83, respectively. CONCLUSIONS: This technology-enabled intervention was feasible and acceptable for individuals with post-stroke EF impairments. Virtual delivery was a key factor in its accessibility and success. The results are promising for improving self-management of vascular risk factors, warranting further study in larger trials.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".