Using the Donabedian framework to examine transitional care for cardiac patients and family caregivers
Bibliographic record
Abstract
PURPOSE: This study aims to examine how health-care managers in acute care and post-acute care facilities support and plan to improve transitional care for cardiac patients and their family caregivers, to better manage care in the home. DESIGN/METHODOLOGY/APPROACH: A qualitative descriptive approach, guided by appreciative inquiry was used in this study. A purposive sample of 16 participants were engaged in the study. Participants completed a demographic questionnaire, the caregiver policy lens questionnaire and participated in one of four focus group interviews. The semi-structured focus group interviews were audio-recorded and analyzed using thematic analysis. FINDINGS: Using Donabedian's framework, six major themes contributed to how health-care managers can improve transitional care: structure included supporting personnel and continuing education; process included enacting approaches of care, coordinating care among the health-care team and calling to work upstream; and outcomes included needing to clarify expectations of home care services and witnessing the impact of the caregiver role. ORIGINALITY/VALUE: These findings demonstrate the importance of Donabedian's core dimensions of structure and processes in influencing caregiver outcomes. These results emphasize the central role of the manager in influencing system change to improve transitional care.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".