The return home model: design and implementation of a geriatric home-care model for long-term care eligible older adults
Bibliographic record
Abstract
BACKGROUND: Most older adults prefer to "age in place" within their communities. This preference cannot always be honored and dependent older adults may transfer to a long-term care facility. The Return Home is an Israel Ministry of Health initiated care model designed to prevent or delay a transfer of the dependent older adult to a long-term facility. The intervention team included a physician, nurse, social worker, occupational therapist, physical therapist, and a dietician, all participating in in-home visits. This study's aim was to examine the Return Home model's feasibility to prevent long-term care placement in a complex, dependent geriatric population. METHODS: We analyzed data from the electronic medical record (EMR) of the provider. Participants were recruited by the Israeli Ministry of Health from July 2021 to November 2022 at the time of hospital discharge. Caregiver input was obtained from interviews at the beginning and end of the one-year intervention. RESULTS: 138 patients were enrolled in the intervention. 86 (62%) completed the intervention in their homes, 39 (28%) died during the intervention, 5 (4%) were transferred to a long-term facility, 8 (6%) were dis-enrolled. Prescription medication usage declined by 0.79 medications per person on average. Forty patients had pressure ulcers at the time of admission; all of these ulcers healed during the program, after an average time of 1.5 months. Caregiver burden measured by the Zarit score, declined from 20.9 to 9.7, t (156) = 11.88, p < 0.001. CONCLUSIONS: The Return Home intervention demonstrated the feasibility of preventing or delaying long-term care placement for a complex, dependent geriatric population. Further evaluation is needed to determine effectiveness and inform broader implementation.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".