Development of a learning module for emergency department nurses to improve geriatric knowledge to guide geriatric patient care
Bibliographic record
Abstract
Background: Canada's population is aging, and the geriatric patient population utilizes emergency departments (ED) at increasingly high numbers. Older patients (65 +) often present to the ED with complex atypical disease presentations, putting them at a higher risk for morbidity and mortality if not recognized. Since the ED model is traditionally designed for rapid assessment and triage, providing effective care to older adults presents many challenges. ED nurses are expected to detect frailty and vulnerability and recognize atypical symptoms of a hidden disease (i.e., geriatric syndromes). However, it is difficult to ensure the delivery of consistent assessments and care in the ED without specific geriatric knowledge and education. Purpose: The purpose of this practicum was to develop a geriatric learning module (GLM) to improve ED nurses' geriatric knowledge to better guide patient care and assessment. Methods: The four methods consisted of 1) an integrated review to identify factors influencing nurses' ability to care for and address the complex needs of older adults visiting the ED and to identify potential strategies to enhance nurses' knowledge and overall geriatric care in the ED, 2) an environmental scan to determine what geriatric resources and policies were available, 3) consultations with ED nurses to identify practice and knowledge issues related to the care of geriatric patients in the ED, and identify educational needs through a questionnaire, 4) the development of the GLM. Results: The literature review identified individual and work environment level factors influencing the care of older adults in ED. Individual-level nursing factors included knowledge gaps, limited experience, negative perceptions, and attitudes. A lack of appropriate physical space and equipment, workload and staff shortages, and ED culture were work environment-level factors. The strategies to improve geriatric care and assessment included education, Geriatric Emergency Management (GEM) nurses, focused nursing assessments, and Geriatric Emergency Department Intervention (GEDI) (i.e., multidisciplinary teams). The environmental scan strengthened the review findings and provided additional information about assessment methods, geriatric educational resources, and the educational needs of nurses. During the consultations, the nurses identified perceived barriers to providing quality care to older ED patients consistent with the literature. Based on the integrated literature review findings, environmental scan, and consultations, a GLM containing six modules and three case studies was developed. Conclusion: The GLM was developed to address the learning needs of the ED nurses and provide the foundational geriatric knowledge and skills to guide geriatric triage, assessment, and care to help improve outcomes for older patients in the ED. The GLM will be incorporated into the onboarding and orientation of nurses joining the ED team.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".