Implementation of the virtual transitional care stroke intervention for older adults with stroke and multimorbidity: A qualitative descriptive study
Bibliographic record
Abstract
Background: Older adults with stroke and multimorbidity experience frequent care transitions, which are often poorly coordinated and fragmented. We conducted a pragmatic randomized controlled trial (RCT) to test the implementation and effectiveness of the Transitional Care Stroke Intervention (TCSI), a 6-month, multi-component, evidence-informed intervention to support older adults with stroke and multimorbidity using outpatient stroke rehabilitation services. The TCSI was designed to support self-management, improve health outcomes, and enhance the quality and experience of care transitions. Objective: To explore the facilitators and challenges to implementing the TCSI, from the perspective of healthcare providers (HCPs) (n = 12) and Managers (n = 3). Methods: Data collection and analysis were guided by the Consolidated Framework for Implementation Research (CFIR). Data were collected from study documents, individual and group interviews conducted with HCPs and a Care Coordinator, and surveys from managers. Data were analyzed using thematic analysis. Results: Intervention implementation was facilitated by: a) strong collaborative and interdependent HCP team relationships, b) dedicated resources (funding, staffing) to support intervention delivery, c) training and ongoing support, customized to individual HCP needs, d) organizational readiness, strong leadership, and effective champions, e) structures to facilitate virtual information-sharing, and f) regular monitoring of intervention implementation. Implementation challenges included: a) COVID-19 related challenges (staff turnover, community service disruptions), b) poor communication with community service providers, c) documentation burden (intervention-related), and d) virtual care delivery. Conclusions: This research enhances understanding of the diversity of factors influencing implementation of the TCSI, and the conditions under which implementation is more likely to succeed.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".