Hospital-to-home care transition for dementia patients and family caregivers : discharge planning and decision-making experience of family caregivers
Bibliographic record
Abstract
Hospital-to-home transitions for persons living with dementia (PLWD) are critical periods that often expose family caregivers (FCGs) to significant decisional, informational, and emotional challenges. This dissertation explored the discharge planning and decision-making experiences of culturally diverse caregivers using longitudinal qualitative data analyzed across two time points. Guided by the Ottawa Decision Support Framework (ODSF), the study examined FCGs’ perceptions of the discharge process, the types and levels of decisions they were engaged in, their support needs, and the clarity they held regarding decision choices available to them. A qualitative descriptive method using a content analysis approach was applied to 30 interviews with FCGs of PLWD, with themes and subthemes mapped to the research aims. Findings revealed that FCGs entered discharge planning with varied expectations, often shaped by cultural values and prior healthcare encounters. While some reported satisfaction with clear communication and early involvement, many described fragmented processes, inconsistent initiation of discharge discussions, and limited preparedness. FCGs were frequently tasked with weighing complex decision options such as home care, rehabilitation, or hospice, without adequate information or structured support. Across time, some FCGs developed greater confidence and clarity, while others faced increasing decisional conflict as PLWD health declined and care needs intensified. The study highlights the central role of nurses and interdisciplinary teams in identifying caregiver decisional needs and providing timely, culturally responsive support. Policies that embed caregiver engagement into discharge standards, alongside interventions such as decision aids, interpreter services, and palliative consults, may reduce disparities and improve care alignment with PLWD values. Despite limitations associated with secondary qualitative analysis and small sample size, the dissertation makes an important contribution by amplifying caregiver voices and documenting how decision-making evolves across the trajectory of dementia care transitions. These findings provide direction for future research, policy, and practice to strengthen caregiver preparedness, reduce readmissions, and promote value-concordant care at home.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".