The Stroke Recovery in Motion Implementation Planner: Mixed Methods User Evaluation
Bibliographic record
Abstract
BACKGROUND: As more people are surviving stroke, there is a growing need for services and programs that support the long-term needs of people living with the effects of stroke. Exercise has many benefits; however, most people with stroke do not have access to specialized exercise programs that meet their needs in their communities. To catalyze the implementation of these programs, our team developed the Stroke Recovery in Motion Implementation Planner, an evidence-informed implementation guide for teams planning a community-based exercise program for people with stroke. OBJECTIVE: This study aimed to conduct a user evaluation to elicit user perceptions of the usefulness and acceptability of the Planner to inform revisions. METHODS: This mixed methods study used a concurrent triangulation design. We used purposive sampling to enroll a diverse sample of end users (program managers and coordinators, rehabilitation health partners, and fitness professionals) from three main groups: those who are currently planning a program, those who intend to plan a program in the future, and those who had previously planned a program. Participants reviewed the Planner and completed a questionnaire and interviews to identify positive features, areas of improvement, value, and feasibility. We used descriptive statistics for quantitative data and content analysis for qualitative data. We triangulated the data sources to identify Planner modifications. RESULTS: A total of 39 people participated in this study. Overall, the feedback was positive, highlighting the value of the Planner's comprehensiveness, tools and templates, and real-world examples. The identified areas for improvement included clarifying the need for specific steps, refining navigation, and creating more action-oriented content. Most participants reported an increase in knowledge and confidence after reading the Planner and reported that using the resource would improve their planning approach. CONCLUSIONS: We used a rigorous and user-centered process to develop and evaluate the Planner. End users indicated that it is a valuable resource and identified specific changes for improvement. The Planner was subsequently updated and is now publicly available for community planning teams to use in the planning and delivery of evidence-informed, sustainable, community-based exercise programs for people with 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 | 0.195 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".