Perceptions of a Transitional Care Model for Older Adults With Multimorbidity and Depressive Symptoms
Bibliographic record
Abstract
Abstract Transitioning from hospital to home is an important healthcare system priority. This paper reports on the qualitative findings from a larger mixed methods study designed to examine the implementation and effectiveness of a new transitional care intervention (Community Assets Supporting Transitions [CAST]). The goal of the CAST intervention is to improve the quality and experience of hospital-to-home transitions for older adults (≥ 65 years) with depressive symptoms and multimorbidity. Semi-structured interviews were completed with a sub-set of intervention group trial participants including 11 older adult participants and 1 caregiver, as well as 4 intervention nurses. A qualitative descriptive design was used to explore the perceived impacts of the CAST intervention on participants and their caregivers. Audio-recorded interviews were transcribed verbatim, with descriptive codes and themes generated using conventional content analysis. Patient participants indicated that the intervention resulted in improved access to information (e.g., medication review) and services (e.g., care coordination) that enhanced their self-management. Participants felt that the home visits and phone visits were valuable and helped to improve their mental health. Intervention nurses described advocating for patients to help achieve their needs. For example, nurses advocated for physiotherapy services to provide additional education to support patient mobility. Understanding patient, caregiver, and provider perceptions of the impact of the CAST intervention will help to identify how to improve the delivery of this transitional care intervention, to bridge the gap between hospital and community care, and to positively impact patient health outcomes.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".