The role of theory to develop and evaluate a toolkit to increase clinical measurement and interpretation of walking speed and distance in adults post-stroke
Bibliographic record
Abstract
The iWalk study showed that 10-meter walk test (10mWT) and 6-minute walk test (6MWT) administration post-stroke increased among physical therapists (PTs) following introduction of a toolkit comprising an educational guide, mobile app, and video. We describe the use of theory guiding toolkit development and a process evaluation. We used the knowledge-to-action framework to identify research steps; and a guideline implementability framework, self-efficacy theory, and the transtheoretical model to design and evaluate the toolkit and implementation process (three learning sessions). In a before-and-after study, 37 of the 49 participating PTs completed online questionnaires to evaluate engagement with learning sessions, and rate self-efficacy to perform recommended practices pre- and post-intervention. Thirty-three PTs and 7 professional leaders participated in post-intervention focus groups and interviews, respectively. All sites conducted learning sessions; attendance was 50-78%. Self-efficacy ratings for recommended practices increased and were significant for the 10mWT (p ≤ 0.004). Qualitative findings highlighted that theory-based toolkit features and implementation strategies likely facilitated engagement with toolkit components, contributing to observed improvements in PTs’ knowledge, attitudes, skill, self-efficacy, and clinical practice. The approach may help to inform toolkit development to advance other rehabilitation practices of similar complexity.Implications for RehabilitationToolkits are an emerging knowledge translation intervention used to support widespread implementation of clinical practice guideline recommendations.Although experts recommend using theory to inform the development of knowledge translation interventions, there is little guidance on a suitable approach.This study describes an approach to using theories, models and frameworks to design a toolkit and implementation strategy, and a process evaluation of toolkit implementation.Theory-based features of the toolkit and implementation strategy may have facilitated toolkit implementation and practice change to increase clinical measurement and interpretation of walking speed and distance in adults post-stroke. Toolkits are an emerging knowledge translation intervention used to support widespread implementation of clinical practice guideline recommendations. Although experts recommend using theory to inform the development of knowledge translation interventions, there is little guidance on a suitable approach. This study describes an approach to using theories, models and frameworks to design a toolkit and implementation strategy, and a process evaluation of toolkit implementation. Theory-based features of the toolkit and implementation strategy may have facilitated toolkit implementation and practice change to increase clinical measurement and interpretation of walking speed and distance in adults post-stroke.
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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 | 0.077 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".