Evaluating Patient Experience in a Community Stroke Rehabilitation Program : Development of a Tool and Evaluation Plan
Bibliographic record
Abstract
Abstract TextBackground: The Southeastern Ontario(SEO) Community Stroke Rehabilitation Program (CSRP) began in 2009. While the program has seen a steady increase in referrals and visits, a consistent approach to the evaluation of patient experience is needed. Methods: A literature review and environmental scan was conducted to identify: 1) existing questionnaires/tools, 2) patient perspectives of essential elements of effective community rehabilitation; and 3) best practices for questionnaire implementation. Results were summarized into themes and used to evaluate content of existing tools. Findings and recommendations were presented to an advisory group of patients, caregivers and service providers. Results: Eight elements of patient experience were identified as essential including measurement of: 1) impact on patientu2019s knowledge; 2) impact on ability to perform daily activities; 3) transition from hospital to home; 4) speed of service delivery; 5) access to further rehabilitation within the community; 6) engagement of patient as active participant in their rehabilitation; 7) patientsu2019 self-rating of recovery; and 8) patient feeling prepared for discharge. These elements were mapped against the content of 3 existing tools and supplemental questions were created to address missing themes. Essential elements were confirmed by the advisory group; questions refined; and recommendations were made for pilot testing.Conclusion: This partnership between students, researchers, knowledge brokers (Stroke Network of SEO), clinicians, stroke survivors and caregivers has produced a practical, evidence-informed program evaluation strategy. Next Steps: A tool incorporating the 8 elements of patient experience and recommendations will be tested for implementation feasibility and final version incorporated into ongoing patient CSRP evaluation.
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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 | 0.194 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".