EVALUATING FUNCTIONAL INDEPENDENCE IN PEOPLE WITH STROKE AND COGNITIVE IMPAIRMENT AFTER AN INTERPROFESSIONAL KNOWLEDGE TRANSLATIONS INTERVENTION, CO-OP KT
Bibliographic record
Abstract
Background: Patients with cognitive impairment (CI) and stroke are often denied access to inpatient rehabilitation due to lack of skills and knowledge to treat stroke patients with CI. To address these issues, we implemented a knowledge translation intervention designed to increase rehabilitation team knowledge and self-efficacy based on the Cognitive Orientation to daily Occupational Performance (CO-OP) approach. The aim of this project is to estimate the odds of achieving minimal clinically important difference (MCID) in functional independence relative to sample cohort, rehabilitation hospital, and cognitive impairment severity in people with stroke.Methods: Five inpatient rehabilitation teams from Toronto, Canada, participated in the intervention that consisted of a 2 day workshop, 4 months of implementation support, health system support, and a sustainability plan. Functional Independence Measure (FIMu00ae) data were extracted from the E-Stroke electronic rehabilitation referral system. Twelve months of data pre-intervention and 6 months post-intervention were analyzed. A logistic regression was performed to determine the odds ratios for achieving MCID based on sample cohort (historical control vs post-intervention), controlling for severity of CI.Results: Odds of achieving MCID were 1.18 for the post-intervention group relative to the historical control; ranged from 0.17 to 0.47 for the rehabilitation hospitals relative to the most successful site; and were 1.23 for mild CI, 1.04 for moderate CI, and 0.50 for severe CI relative to no cognitive impairment. Conclusions: Additional work is required to improve functional independence in people with stroke and severe CI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".