Shaping older adults’ care policy: a scoping review of key determinants in post-acute and community reintegration transitions
Bibliographic record
Abstract
BACKGROUND: The aging population is driving an increasing demand for long-term care (LTC) and complex continuing care (CCC) across member countries of the Organization for Economic Co-operation and Development (OECD). Addressing this growing need requires improved care transition strategies that prioritize directing older adults with less resource-intensive needs toward home care rather than LTC or CCC. Effective implementation of these strategies necessitates that decision-makers have a comprehensive understanding of the factors influencing older adults’ transitions across care settings. Although substantial research has investigated these factors, the current understanding remains fragmented due to the limited synthesis of recent evidence. OBJECTIVES: This scoping review identifies factors that influence older adults’ transitions across two critical pathways: (1) post-acute care transitions (from acute care to LTC, CCC, and home care) and (2) community reintegration transitions (from LTC and CCC to home care). The findings aim to inform evidence-based integration of these factors into care models and placement decisions. METHODS: Using Arksey and O’Malley’s five-stage framework, we reviewed English-language publications from OECD countries between 2015 and 2025 across SCOPUS, MEDLINE, OVID EMBASE, CINAHL EBSCO, and Web of Science. RESULTS: Our review of 120 publications identified socio-demographic characteristics, caregiver support, health conditions, healthcare system attributes, funding policies, and person-centered care as key determinants of older adults’ transitions. CONCLUSIONS: Our review underscores the importance of incorporating the identified determinants into care models to address older adults’ individualized needs and support optimal placement decisions. This evidence-based approach can guide policy reforms and management practices, improving resource utilization and system efficiency. Additionally, we outline key gaps in the literature and propose directions for future research.
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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 | 0.026 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.025 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".