A qualitative evaluation of a new community living model: medical foster home placement
Bibliographic record
Abstract
BACKGROUND: The Department of Veterans Affairs (VA) Medical Foster Home (MFH) program is an alternative to nursing home (NH) placement for veterans who are NH-eligible but prefer to receive care in private homes. Program expansion is ongoing; however, inefficiencies exist in targeting Veterans for enrollment. METHODS: Semi-structured interviews were conducted with 35 individuals, 9 national MFH coordinators, 14 Primary Care Team Members, 2 veterans living in MFHs, 4 of their family members, 3 caregivers, and 3 family members of veterans who declined to participate in the program. Transcripts were analyzed using a general inductive approach supported by Archive for Technology, Lifeworld and Everyday language, text interpretation (ATLAS ti) V6.2. RESULTS: Three themes were identified as key facilitators of successful MFH placement: 1) The Environment - veterans needed to have appropriate and comfortable physical space that ensured safety and was in a desirable geographic location; 2) The Match - a collaborative relationship between veterans, care providers, and the medical team providing care within the home was essential; and 3) Perceptions and Expectations about the ability for needs to be met in MFHs and challenges related to health concerns, relocation, and costs. These themes, when integrated into the Social Ecological Model, provide a theoretical framework from which to guide future research and understand policy implications. CONCLUSIONS: MFHs represent a novel alternative to NH placement. This evaluation provides an understanding of factors that lead to successful MFH placement and integrates these themes into a theoretical framework designed to assist both VA policymakers interested in expanding the program and those within the civilian community seeking to study alternatives to traditional NH 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 | 0.034 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".