Exemplary post-discharge stroke rehabilitation programs: A multiple case study
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to identify essential aspects of exemplary post-discharge stroke rehabilitation as perceived by patients, care partners, rehabilitation providers, and administrators. DESIGN: We carried out an exploratory qualitative, multiple case study. Stroke network representatives from four regions of the province of Ontario, Canada each nominated one post-discharge rehabilitation program they felt was exemplary. SETTING: The programs included: a mixed home- and clinic-based service; a home-based service; a clinic-based service with a stroke community navigator and; an out-patient clinic-based service. PARTICIPANTS: Participants included 32 patients, 16 of their care partners, 23 providers, and 5 administrators. METHODS: We carried out semi-structured qualitative interviews with patients and care partners, focus groups with providers, and semi-structured interviews with administrators. Health records of patient participants were reviewed. Using an interpretivist-informed inductive content analysis, we developed overarching categories and subcategories first for each program and then across programs. RESULTS: Across four regions with differing types of programs, exemplary care was characterized by three essential components: stroke and stroke rehabilitation knowledge, relationship built through personalized respectful care, and a commitment to high quality, person-centered care. CONCLUSION: Exemplary post-discharge care included knowledge regarding identification and treatment of stroke-related impairment, that is, information found in best practice guidelines. However, expertise related to building relationship through providing personalized respectful care, within a mutually supportive, improvement-oriented team was also essential. Additionally, administrators played a crucial role in ensuring continued ability to deliver exemplary care.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".