International Experiences in Respite Care and Insights for China: A Scoping Review Based on Health System Framework
Bibliographic record
Abstract
Abstract Background As China faces the rapid aging challenge, integrating global best practices into respite care systems is of great urgency. Methods A systematic review was conducted using “Respite Care” and the four-dimension framework of Health System (service delivery, financing and payment, resource supply and regulation) across PubMed, CNKI, and government portals through February 20, 2025. From 483 records, 23 studies were selected and analyzed. Results Analyses revealed diverse respite care models in Europe, North America, Australia. Service delivery: The U.S. NFCSP offers adult day care and emergency respite, Denmark’s “All In One” flexible home visits, and employment support initiatives in the UK and Sweden. Financing and payment: Australia’s RRC caps costs under federal funding, Germany splits insurance costs between employers and employees, while the U.S. relies on federal and state funding. Resource supply In terms of workforce, Canada’s PRISMA program integrates community-based respite care via case managers, while the U.S. PACE model deploys multidisciplinary teams. Regulation The UK’s Care Act and the U.S. Lifespan Respite Care Act ensure quality and accessibility. Implications China’s pilot programs in Beijing and Hangzhou suggest future priorities: scaling community respite centers with diversified services; building professional-informal caregiver networks; expanding long-term care insurance coverage through public-private financing; and establishing quality standards with third-party evaluations. Conclusion International experiences emphasize four-dimension system synergy for respite care. China’s path forward lies in adapting these insights to its familial care traditions, fostering a government-led, market-engaged, and socially supported respite care model to cope with the surging wave of population aging.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.024 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".