Strategies to Improve Emergency Transitions From Long-Term Care Facilities: A Scoping Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Older adults residing in residential aged care facilities (RACFs) often experience substandard transitions to emergency departments (EDs) through rationed and delayed ED care. We aimed to identify research describing interventions to improve transitions from RACFs to EDs. RESEARCH DESIGN AND METHODS: In our scoping review, we included English language articles that (a) examined an intervention to improve transitions from RACF to EDs; and (b) focused on older adults (≥65 years). We employed content analysis. Dy et al.'s Care Transitions Framework was used to assess the contextualization of interventions and measurement of implementation success. RESULTS: Interventions in 28 studies included geriatric assessment or outreach services (n = 7), standardized documentation forms (n = 6), models of care to improve transitions from RACFs to EDs (n = 6), telehealth services (n = 3), nurse-led care coordination programs (n = 2), acute-care geriatric departments (n = 2), an extended paramedicine program (n = 1), and a web-based referral system (n = 1). Many studies (n = 17) did not define what "improvement" entailed and instead assessed documentation strategies and distal outcomes (e.g., hospital admission rates, length of stay). Few authors reported how they contextualized interventions to align with care environments and/or evaluated implementation success. Few studies included clinician perspectives and no study examined resident- or family/friend caregiver-reported outcomes. DISCUSSION AND IMPLICATIONS: Mixed or nonsignificant results prevent us from recommending (or discouraging) any interventions. Given the complexity of these transitions and the need to create sustainable improvement strategies, future research should describe strategies used to embed innovations in care contexts and to measure both implementation and intervention success.
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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 | 0.015 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".