Post-hospitalization transition to home: Patient perspectives of a personalized approach
Bibliographic record
Abstract
Objective: Successful transition from hospital to home for persons having multiple chronic illnesses is vital for improved health and reduction of hospital readmissions. This qualitative study was undertaken to explore patients’ experiences with tailored care transition interventions in order to improve future interventions in a planned larger study. Methods: Eighteen patients were interviewed either individually or in focus groups. Patients had previously completed a larger study that evaluated the impact of post-hospital discharge care transitions interventions, which were tailored to cognitive level and patient activation status. Data were analyzed using qualitative, thematic analysis techniques. Results: The overarching theme identified as a result of the qualitative interviews was: Tailoring Interventions to Address the Complexity of Multiple Chronic Illnesses. It included Checking in or checking out: Patient activation and self-management of chronic illness; Increasing complexity: Management of medications for chronic illness; and Paving a path through complexity with caring. These themes were found in all participants, across all groups of the interventions. Conclusions: Tailored interventions, which included individual assessment of needs and development and implementation of a tailored self-management plan, were viewed as effective by patients for self-management of chronic illness, particularly medication reconciliation and weekly 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".