ADVANCING TRANSITIONAL CARE FOR COMMUNITY-BASED ADULTS WITH STROKE AND MULTIPLE CHRONIC CONDITIONS
Bibliographic record
Abstract
To examine the feasibility and acceptability of a 6-month transitional care intervention linking outpatient rehabilitation and community-based care, and to explore its preliminary effects on patient and provider outcomes and health service use and costs for older adults with stroke and multiple chronic conditions. A mixed-methods study (QUAL + quant) was used to examine the feasibility of the intervention and to explore its effects on quality of life, self-efficacy, depression, anxiety and health service use and costs. The tailored 6-month stroke rehabilitation intervention was delivered by an interprofessional (IP) team and supported by a web-based app, ‘My Stroke Care (MYST)’. Of the 30 participants, 60% had ≥ 6 chronic conditions, 47% had depressive symptoms, 13% had moderate to severe anxiety, and 54% were hospitalized for ≥16 days in the past 6 months. Preliminary findings suggest the intervention is feasible and acceptable to the IP team and older adult participants. Providers perceived the following benefits associated with the intervention: increased awareness and use of community resources, increased communication and coordination between inpatient and outpatient teams, increased use of stroke best practices, increased understanding of patients’ needs, and increased focus on patient-oriented goals and strengths. Significant improvement was seen in patients’ self-efficacy (p=0.04) and a reduction in hospitalizations (p< 0.0001) over the study period. The findings support the feasibility and preliminary effectiveness of the intervention in improving patient outcomes, building capacity among an outpatient rehabilitation team, and using MYST as a platform for information sharing and goal setting.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".