MétaCan
Menu
Back to cohort
Record W4395118121 · doi:10.1093/geront/gnae036

Strategies to Improve Emergency Transitions From Long-Term Care Facilities: A Scoping Review

2024· review· en· W4395118121 on OpenAlexaff
Kaitlyn Tate, Greta G. Cummings, Frode F. Jacobsen, Gayle Halas, Graziella Van den Bergh, Rashmi Devkota, Shovana Shrestha, Malcolm Doupe

Bibliographic record

VenueThe Gerontologist · 2024
Typereview
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of ManitobaUniversity of Alberta
FundersNorges Forskningsråd
KeywordsTerm (time)Long-term careBusinessProcess managementMedical emergencyRisk analysis (engineering)MedicineNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.422
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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.

Opus teacher head0.151
GPT teacher head0.499
Teacher spread0.348 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations7
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueThe GerontologistSame topicGeriatric Care and Nursing HomesFrench-language works237,207