Experiences of Physical Therapists and Professional Leaders With Implementing a Toolkit to Advance Walking Assessment Poststroke: A Realist Evaluation
Bibliographic record
Abstract
OBJECTIVE: The iWalk study showed significant increase in use of the 10-Meter Walk Test (10MWT) and 6-Minute Walk Test (6MWT) poststroke following provision of a toolkit. This paper examined the influence of contextual circumstances on use of the toolkit and implementation strategy across acute care and inpatient and outpatient rehabilitation settings. METHODS: A theory-based toolkit and implementation strategy was designed to support guideline recommendations to use standardized tools for evaluation of walking, education, and goal-setting poststroke. The toolkit comprised a mobile app, video, and educational guide outlining instructions for 3 learning sessions. After completing learning sessions, 33 physical therapists and 7 professional leaders participated in focus groups or interviews. As part of a realist evaluation, the study compared and synthesized site-specific context-mechanism-outcome descriptions across sites to refine an initial theory of how the toolkit would influence practice. RESULTS: Analysis revealed 3 context-mechanism-outcomes: (1) No onsite facilitator? No practice change in acute care: Without an onsite facilitator, participants lacked authority to facilitate and coordinate the implementation strategy; (2) Onsite facilitation fostered integration of select practices in acute care: When onsite facilitation occurred in acute care, walk test administration and use of reference values for patient education were adopted variably with high functioning patients; (3) Onsite facilitation fostered integration of most practices in rehabilitation settings: When onsite facilitation occurred, many participants incorporated 1 or both tests to evaluate and monitor walking capacity, and reference values were applied for inpatient and outpatient education and goal setting. Participants preferentially implemented the 10MWT over the 6MWT because set-up and administration were easier and a greater proportion of patients could walk 10 m. CONCLUSION: Findings underscore contextual factors and activities essential to eliciting change in assessment practice in stroke rehabilitation across care settings. IMPACT: This study shows that to foster recommended walking assessment practices, an onsite facilitator should be present to enable learning sessions and toolkit use.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".