Geriatric emergency management: An improved approach to care of high risk older adults in the emergency department
Bibliographic record
Abstract
O adults comprise the fastest growing demographic in Canada, and represent a large number of patient visits to the emergency department (ED). The complexity of these patients frequently results in the consumption of more ED resources, high rates of return ED visits, and more frequent admissions to hospital. However, despite the frequency of visits the unique needs of older adults may be difficult to realize within the fast-paced ED environment. Additionally, current system demands result in increased pressure to rapidly triage, assess and treat older patients. These factors, together with the existing knowledge gap regarding geriatric specific interventions, has led to an ED environment that is frequently unreceptive and poorly adapted to meet the needs of older adults. Ultimately this impacts the patient experience and ED use, and may contribute to decline and loss of independence. The Geriatric Emergency Management (GEM) program was developed to optimize the treatment, safety and independence of identified high-risk seniors who are discharged home from the ED. GEM is an evidence-based, collaborative initiative between the ED and the Regional Geriatric Program of Eastern Ontario, and includes more than 20 community partners in the planning of care strategies and supports. This presentation will provide an overview of the innovative clinical, education, research & program strategies utilized to build capacity within the ED to achieve successful outcomes with this population. The success of this program has led to regional program expansion in order to promote an improved approach to the management of older adults in the ED. Laura Wilding et al., J Nurs Care 2013, 2:3 http://dx.doi.org/10.4172/2167-1168.S1.004
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
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".