Applying the Knowledge-to-Action Framework to Implement Gait and Balance Assessments in Inpatient Stroke Rehabilitation
Bibliographic record
Abstract
OBJECTIVES: The overall objectives of this project were to implement and sustain use of a gait assessment battery (GAB) that included the Berg Balance Scale, 10-meter walk test, and 6-minute walk test during inpatient stroke rehabilitation. The study objective was to assess the effect of the study intervention on clinician adherence to the recommendations and its effect on clinician perceptions and the organization. DESIGN: Pre- and post-training intervention study. SETTING: Subacute inpatient rehabilitation facility. PARTICIPANTS: Physical therapists (N=6) and physical therapist assistants (N=2). INTERVENTION: The intervention comprised a bundle of activities, including codeveloping and executing the plan with clinicians and leaders. The multicomponent implementation plan was based on the Knowledge-to-Action Framework and included implementation facilitation, implementation leadership, and a bundle of knowledge translation interventions that targeted barriers. Implementation was an iterative process in which results from one implementation phase informed planning of the next phase. MAIN OUTCOME MEASURES: Clinician administration adherence, surveys of perceptions, and organizational outcomes. RESULTS: Initial adherence to the GAB was 46% and increased to more than 85% after 6 months. These adherence levels remained consistent 48 months after implementation. Clinician perceptions of measure use were initially high (>63%), with significant improvements in knowledge and use of one measure after implementation. CONCLUSIONS: We successfully implemented the assessment battery with high levels of adherence to recommendations, likely because of using the bundle of knowledge translation activities, facilitation, and use of a framework to codevelop the plan. These changes in practice were sustainable, as determined by a 4-year follow-up.
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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 | 0.038 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".