Lessons learned and functional outcomes following multifaceted team training in a cognitive strategy-based approach to stroke rehabilitation
Bibliographic record
Abstract
AIM: To estimate the impact of a multifaceted knowledge translation intervention on patient rehabilitation outcomes in an inpatient stroke setting. METHODS: Interprofessional stroke rehabilitation teams were trained in Cognitive Orientation to daily Occupational Performance as part of the larger knowledge translation study. This study describes a two group (historical control vs. post knowledge translation intervention) nonrandomized study. Patient participants with stroke and cognitive impairment were recruited from five rehabilitation hospitals and completed an assessment battery upon admission to and discharge from rehabilitation and at three follow-up times. Data were analyzed using a two-way ANOVA. RESULTS: Seventeen historical controls and eight postintervention participants were recruited. The effect for time was significant (P ≤ 0.05) for the majority of outcomes, but there were no significant group × time effects. Small effect sizes for the group × time interaction were noted on several indicators. CONCLUSION: Few studies report on patient outcomes following a knowledge translation intervention. Small effect sizes were detected on several patient outcomes, despite study limitations. Lessons learned for future patient-level studies within knowledge translation interventions include the importance of monitoring therapist adherence to implementation protocols and ensuring research designs consider the impact on patient recruitment and retention.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".