<scp>Connect‐Home</scp> transitional care from skilled nursing facilities to home: A stepped wedge, cluster randomized trial
Bibliographic record
Abstract
BACKGROUND: Skilled nursing facility (SNF) patients and their caregivers who transition to home experience complications and frequently return to acute care. We tested the efficacy of the Connect-Home transitional care intervention on patient and caregiver preparedness for care at home, and other patient and caregiver-reported outcomes. METHODS: We used a stepped wedge, cluster-randomized trial design to test the intervention against standard discharge planning (control). The setting was six SNFs and six home health offices in one agency. Participants were 327 dyads of patients discharged from SNF to home and their caregivers; 11.1% of dyads in the control condition and 81.2% in the intervention condition were enrolled after onset of COVID-19. Patients were 63.9% female and mean age was 76.5 years. Caregivers were 73.7% female and mean age was 59.5 years. The Connect-Home intervention includes tools, training, and technical assistance to deliver transitional care in SNFs and patients' homes. Primary outcomes measured at 7 days included patient and caregiver measures of preparedness for care at home, the Care Transitions Measure-15 (patient) and the Preparedness for Caregiving Scale (caregiver). Secondary outcomes measured at 30 and 60 days included the McGill Quality of Life Questionnaire, Life Space Assessment, Zarit Caregiver Burden Scale, Distress Thermometer, and self-reported number of patient days in the ED or hospital in 30 and 60 days following SNF discharge. RESULTS: The intervention was not associated with improvement in patient or caregiver outcomes in the planned analyses. Post-hoc analyses that distinguished between pre- and post-pandemic effects suggest the intervention may be associated with increased patient preparedness for discharge and decreased number of acute care days. CONCLUSIONS: Connect-Home transitional care did not improve outcomes in the planned statistical analysis. Post-hoc findings accounting for COVID-19 impact suggest SNF transitional care has potential to increase patient preparedness and decrease return to acute 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".