The impact of an integrated, interprofessional knowledge translation intervention on access to inpatient rehabilitation for persons with cognitive impairment
Bibliographic record
Abstract
INTRODUCTION: Stroke rehabilitation teams' skills and knowledge in treating persons with cognitive impairment (CI) contribute to their reduced access to inpatient rehabilitation. This study examined stroke inpatient rehabilitation referral acceptance rates for persons with CI before and after the implementation of a multi-faceted integrated knowledge translation (KT) intervention aimed at improving clinicians' skills in a cognitive-strategy based approach, Cognitive Orientation to daily Occupational Performance (CO-OP), CO-OP KT. METHODS: CO-OP KT was implemented at five inpatient rehabilitation centres, using an interrupted time series design and data from an electronic referral and database system called E-Stroke. CO-OP KT included a 2-day workshop, 4 months of implementation support, health system support, and a sustainability plan. A mixed effects model was used to model monthly acceptance rates for 12 months prior to the intervention and 6 months post. RESULTS: The dataset was comprised of 2604 pre-intervention referrals and 1354 post. In the mixed effects model, those with CI had a lower pre-intervention acceptance rate than those without. Post-intervention the model showed the acceptance rate for those with CI increased by 8.6% (p = 0.02), whereas those with no CI showed a non-significant increase of less than 1%. CONCLUSIONS: Proportionally more persons with CI gained access to inpatient stroke rehabilitation following an integrated KT intervention.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".