The Impact of Geriatric Emergency Management Nurses on the Care of Frail Older Patients in the Emergency Department: a Systematic Review
Bibliographic record
Abstract
BACKGROUND: Frail older adults are high users of emergency departments (EDs). Many Canadian EDs have hired Geriatric Emergency Management (GEM) nurses in an effort to improve care to older adults. METHODS: We conducted a systematic review to determine the impact of GEM nurses on care provided to frail older adults. We searched MEDLINE, Embase, CINAHL, and Cochrane databases. A grey literature search was also conducted. Inclusion criteria were English-language, evaluation of GEM nurse or geriatric-trained nurse assessments of older adults (age ≥ 65 years) within the ED, and reported clinical and/or health system outcomes. The PRISMA statement was followed, and article quality was assessed using GRADE. RESULTS: 5,115 citations and 191 full text articles were screened; 8 articles from 7 different studies were included. Study quality varied between very low to high. Five included studies analyzed the effect of GEM nurses on ED revisits, with most finding they decreased revisits. Four included studies analyzed the effect of GEM nurses on hospital admissions/readmissions, demonstrating variable impact. One study looked at the cost-effectiveness and found the cost to be negligible. The impact on patient-specific outcomes was less clear. CONCLUSIONS: GEM nurses may be an effective option to help in the management of frail older adults in the ED.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".