Barriers to implementation of stroke rehabilitation evidence: findings from a multi-site pilot project
Bibliographic record
Abstract
Purpose: To describe the barriers to implementation of evidence-based recommendations (EBRs) for stroke rehabilitation experienced by nurses, occupational therapists, physical therapists, physicians and hospital managers. Methods: The Stroke Canada Optimization of Rehabilitation by Evidence project developed EBRs for arm and leg rehabilitation after stroke. Five Canadian stroke inpatient rehabilitation centers participated in a pilot implementation study. At each site, a clinician was identified as the “local facilitator” to promote the 6-month implementation. A research coordinator observed the process. Focus groups done at completion were analyzed thematically for barriers by two raters. Results: A total of 79 rehabilitation professionals (23 occupational therapists, 17 physical therapists, 23 nurses and 16 directors/managers) participated in 21 focus groups of three to six participants each. The most commonly noted barrier to implementation was lack of time followed by staffing issues, training/education, therapy selection and prioritization, equipment availability and team functioning/communication. There was variation in perceptions of barriers across stakeholders. Nurses noted more training and staffing issues and managers perceived fewer barriers than frontline clinicians. Conclusions: Rehabilitation guideline developers should prioritize evidence for implementation and employ user-friendly language. Guideline implementation strategies must be extremely time efficient. Organizational approaches may be required to overcome the barriers.Implications for RehabiliationDespite increasingly strong evidence for stroke rehabilitation, there are delays in implementation into clinical practice.This study showed that lack of time, staffing issues, staff education, therapy selection or prioritization, lack of equipment and team functioning were the main barriers to implementation.Managers and stakeholders should consider these barriers and prioritize evidence when implementing.
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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 | 0.068 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".