A multi-faceted knowledge translation approach to support persons with stroke and cognitive impairment: evaluation protocol
Bibliographic record
Abstract
BACKGROUND: Patients with cognitive impairments following a stroke are often denied access to inpatient rehabilitation. The few patients with cognitive impairment admitted to rehabilitation generally receive services based on outdated impairment-reduction models, rather than recommended function-based approaches. Both reduced access to rehabilitation and the knowledge-to-practice gap stem from a reported lack of skills and knowledge regarding cognitive rehabilitation on the part of inpatient rehabilitation team members. To address these issues, a multi-faceted knowledge translation (KT) initiative will be implemented and evaluated. It will be targeted specifically at the inter-professional application of the cognitive orientation to daily occupational performance (CO-OP). CO-OP training combined with KT support is called CO-OP KT. The long-term objective of CO-OP KT is to optimize functional outcomes for individuals with stroke and cognitive impairments. Three research questions are posed: 1. Is the implementation of CO-OP KT associated with a change in the proportion of patients with cognitive impairment following a stroke accepted to inpatient rehabilitation? 2. Is the implementation of CO-OP KT associated with a change in rehabilitation clinicians' practice, knowledge, and self-efficacy related to implementing the CO-OP approach, immediately following and 1 year later? 3. Is CO-OP KT associated with changes in activity, participation, and self-efficacy to perform daily activities in patients with cognitive impairment following stroke at discharge from inpatient rehabilitation and at 1-, 3-, and 6-month follow-ups? METHODS/DESIGN: Three interrelated studies will be conducted. Study 1 will be a quasi-experimental, interrupted time series design measuring monthly summaries of stroke unit level data. Study 2, which relates to changes in health care professional practice and self-efficacy, will be a single group pre-post evaluation design incorporating chart audits and a self-report survey. Study 3 will assess patient functional outcomes using a non-randomized design with historical controls. Assessments will occur during admission and discharge from rehabilitation and at 1, 3, and 6 months following discharge from rehabilitation. DISCUSSION: This project will advance knowledge about the degree to which the implementation of a supported KT initiative can sustainably change health system, knowledge, and patient outcomes.
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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 | 0.041 | 0.026 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.044 | 0.009 |
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".