The Impact of Community‐Hospital‐Family Interactive Management on Social Isolation in Elderly Patients Undergoing Maintenance Hemodialysis
Bibliographic record
Abstract
OBJECTIVE: The primary objective was to evaluate the impact of the Community-Hospital-Family Interactive Management (CHFIM) Model on social isolation in elderly patients undergoing maintenance hemodialysis. Secondary objectives included assessing its effects on social support, loneliness, depression, and family function. METHODS: A total of 160 elderly maintenance hemodialysis patients from the Blood Purification Center of Taixing People's Hospital between July 2023 and March 2024 were selected as the study subjects. Using a controlled trial design, the patients were divided into a control group (n = 80) and an intervention group (n = 80). The control group received routine care, while the intervention group received Community-Hospital-Family Interactive Management. The social network level, social support level, loneliness, and depression levels of the two groups were compared. RESULTS: After the intervention, the intervention group showed significant improvements in social network level (primary outcome) and social support, along with significant reductions in loneliness and depression (secondary outcomes) compared to the control group (p < 0.05). CONCLUSION: The CHFIM Model effectively reduces social isolation in elderly maintenance hemodialysis patients, promoting their physical and mental health. TRIAL REGISTRATION: Chinese Clinical Trial Registry, ChiCTR2500107611.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".