White Paper—Geriatric Emergency Medicine Education: Current State, Challenges, and Recommendations to Enhance the Emergency Care of Older Adults
Bibliographic record
Abstract
Older adults account for 25% of all emergency department (ED) patient encounters. One in five Americans will be 65 or older by 2030. In response to this need, geriatric emergency medicine (GEM) has developed into a robust area of academic and clinical interest, with extensive evidence-based research and guidelines, including clear undergraduate and postgraduate GEM competencies. Despite these developments, GEM content remains underrepresented in curricula and licensing examinations. The complex reasons for these deficits include a perception that care of older adults is not a core emergency medicine (EM) competency, a disjunction between traditional definitions of expertise and the GEM perspective, and lack of curricular capacity. This White Paper, prepared on behalf of the Academy of Geriatric Emergency Medicine, describes the state of GEM education, identifies the challenges it faces, and reviews innovations, including research presented at the 2018 Society for Academic Emergency Medicine (SAEM) Annual Scientific Meeting. The authors propose a number of recommendations. These include recognizing GEM as a core educational priority in EM, enhancing academic support for GEM clinician-educators, using social learning and practical problem solving to teach GEM concepts, emphasizing a whole-person multisystem approach to care of older adults, and identifying ageist attitudes as a hurdle to safe and effective GEM care.
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 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 | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".