Stroke and Cognitive Impairment : a Cognitive Strategy-Based Approach To Improve Access To inpatient Stroke Rehabilitation
Bibliographic record
Abstract
Background Access to rehabilitation for persons with stroke and cognitive impairment (CI) is limited. Rehabilitation teamsu2019 skills and knowledge to treat stroke patients with CI has been identified as a contributing factor. This study aimed to examine inpatient stroke rehabilitation acceptance rates for referred persons with CI before and after the implementation of a knowledge translation (KT) initiative aimed at improving skills in the cognitive-strategy based approach known as Cognitive Orientation to daily Occupational Performance (CO-OP). CO-OP KT includes a workshop, 4-months implementation support, health system support, and a sustainability process. MethodFive inpatient rehabilitation teams from a large Canadian city participated in CO-OP KT. Referrals and acceptances were extracted from the E-Stroke Rehabilitation Referral System for 12 months pre CO-OP KT and 6 months post. A chi-squared analysis was completed comparing acceptance for patients across CI severity levels (mild, moderate, severe) and across sites. ResultsSignificantly more patients with CI were accepted into rehabilitation across all sites post intervention (75.3% pre, 79.9% post; p=0.002), however site-specific differences were noted (-2.6% to 10.2% change in acceptances). The largest overall changes were seen in those with moderate CI (74.3% pre, 80.8% post; p=0.004). There was no significant change for patients without CI (77.8% pre, 75.5% post; p=0.421). ConclusionMore stroke patients with CI received inpatient stroke rehabilitation following implementation of a multi-faceted KT supported cognitive strategy-based intervention. Further analysis is required to determine the relationship between site differences and improvements noted.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".