Shared Decision Making About Housing Transitions for Persons With Dementia: A Four-Case Care Network Perspective
Bibliographic record
Abstract
BACKGROUND: Persons with dementia (PWDs) and their caregivers often face difficult housing decisions, that is, decisions about their living arrangements, in which the perspectives of all members of the care network should be involved. OBJECTIVE: We performed a qualitative data analysis to assess the extent to which housing decisions for PWDs with their formal and informal caregivers correspond to an interprofessional shared decision making (IP-SDM) approach, and what light this approach sheds on their experiences with decision making. RESEARCH DESIGN AND METHODS: We used the IP-SDM model to content-code and analyze data from 4 care networks, each consisting of a PWD, 2 informal and 2 formal caregivers. RESULTS: Decision making in all networks corresponded to most IP-SDM elements, but never included all network members. Decision making was guided by the wishes of the PWD, but their actual involvement decreased over time. DISCUSSION: Results show that while the IP-SDM model was helpful, the options change with cognitive decline and moving to a nursing home can become inevitable in spite of preferences. IMPLICATIONS: Timely and honest communication helps to mitigate the distress of deciding against patient preferences, as could advance care planning about future housing transitions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".