Geriatric Emergency Medicine Fellowships: Current State of Specialized Training for Emergency Physicians in Optimizing Care for Older Adults
Bibliographic record
Abstract
Improving emergency department (ED) care for older adults is a critical issue in emergency medicine. Institutions throughout the United States and Canada have recognized the growing need for a workforce of emergency physician (EP) leaders focused on clinical innovation, education, and research and have developed specialized fellowship training in geriatric emergency medicine (GEM). We describe here the overview, structure, and curricula of these fellowships as well as successes and challenges they have encountered. Seven GEM fellowships are active in the United States and Canada, with five offering postresidency training only, one offering fellowship training during residency only, and one offering both. The backbone of the curriculum for all fellowships is the achievement of core competencies in various aspects of GEM, and each includes clinical rotations, teaching, and a research project. Evaluation strategies and feedback have allowed for significant curricular changes as well as customization of the fellowship experience for individual fellows. Key successes include an improved collaborative relationship with geriatrics faculty that has led to additional initiatives and projects and former fellows already becoming regional and national leaders in GEM. The most critical challenges have been ensuring adequate funding and recruiting new fellows each year who are interested in this clinical area. We believe that interest in GEM fellowships will grow and that opportunities exist to combine GEM fellowship training with a focus in research, administration, or health policy to create unique new types of highly impactful specialized training. Future research may include exploring former fellows' postfellowship experiences, careers, accomplishments, and contributions to GEM to better understand the impact of GEM fellowships.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".