Factors Influencing Older Persons’ Transitions to Facility-Based Care Settings: A Scoping Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Older adults (≥65 years) are living longer with complex health needs and wish to remain at home as their care needs change. We aimed to determine which factors influence older persons' transitions from home living to facility-based care (FBC) settings such as long-term care facilities or assisted living. RESEARCH DESIGN AND METHODS: Through a scoping review of 7 databases, we considered all academic literature examining factors influencing transitions from home living to FBC. Only English articles were reviewed. Based on the Meleis' Health Transition (MHT) model, we categorized findings into: (a) transition conditions; (b) patterns of response; and (c) health services and interventions. RESULTS: We included 204 unique studies. Age, cognitive/functional impairments, and caregiver burden were the most consistent risk factors for older persons' transitions to FBC. Caregiver burden was the only consistent risk factor in both quantitative and qualitative literature. Other factors around health service use or nonmedical factors were examined in a small number of studies, or demonstrated mixed or nonsignificant results. Key research gaps relate to transitions to intermediate levels of FBC, research in public health systems, and research employing qualitative and interventional methods. DISCUSSION AND IMPLICATIONS: We expanded the MHT model to capture informal caregivers and their critical role in transitions from home to FBC settings. More research is needed to address practical needs of clients and caregivers while at home, and self-directed care funding models could be expanded. Theory-driven interventional research focusing on caregivers and successful hospital discharge is critically needed.
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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 | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".