Implementing Caregiver Support Programs in a Regional Stroke System
Bibliographic record
Abstract
Background and Purpose- Family caregivers play a central role in the recovery of people with stroke. They need support to optimize the care they provide and their own health and well-being. Despite support from the literature and best practice recommendations, healthcare systems are not formally adopting caregiver programs. This study aimed to describe system-level facilitators and barriers to caregiver support program implementation in a regional stroke system. Methods- Using a qualitative descriptive study design, focus groups were conducted with regional rehabilitation specialists, education coordinators, community and long-term care specialists, and regional/district program directors. Semi-structured interviews were conducted with regional medical directors, health professionals providing stroke care in acute care, rehabilitation and community settings, regional health executives, and primary care leaders. Data were analyzed using inductive thematic analysis. Results- Four focus groups (n=43) and 29 interviews were conducted. We identified 4 themes related to caregiver program implementation: (1) establishing the need for caregiver education and support in an integrated healthcare system; (2) incorporating caregiver programs into the system of care across the care continuum; (3) uncertainty regarding ownership and responsibility for implementation; and (4) addressing regional variations related to access, availability, and culture. Conclusions- This study provides a comprehensive understanding of organization and system-level considerations for implementing caregiver programs in a regional stroke system. Program implementation requires evidence to establish the need for caregiver programs, practical strategies, and establishing ownership to incorporate programs into existing healthcare systems, and consideration of regional variations across healthcare systems. Ultimately, adopting programs to support caregivers will improve recovery in people with stroke and caregiver well-being.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".