Impact of Geriatric Emergency Fellowship Training on the careers of Emergency Physicians
Bibliographic record
Abstract
Introduction The geriatric population continues to increase and will impact the emergency department (ED). Older adult patients require different care from other groups of patients. Hence, it is essential to create a workforce that specializes in geriatric emergency medicine (GEM). Geriatric emergency medicine fellowships were developed to serve this need. However, despite 20 years since the creation of GEM fellowships, it is not known how GEM fellowships have impacted the career of graduates of GEM fellowships. The goal of this study is to examine the impact of these geriatric emergency fellowship training programs on the career of geriatric emergency fellows. Methods We surveyed the emergency physicians who had graduated from GEM fellowship programs in the US and Canada by using a 36-question, web-based questionnaire. The survey was pilot-tested on five GEM experts, fellowship graduates, and a GEM fellowship director. Result We had a 68% survey completion rate, two partially answered the study. All participants reported that they continue to have GEM as a part of his/her career. More than half either received grants, published papers, helped establish GEM divisions or caring in their hospital, and worked beyond clinical work in the ED, including academic and administrative fields. More than 80% reported that their fellowship helped obtain their current positions and was helpful in career progression. Approximately two-thirds were satisfied with their current work/life balance. Conclusion The GEM fellowship training has been impactful in the careers of former GEM fellows and has contributed to many becoming leaders in GEM clinical service, administration, education, and research. It can serve as a stepping stone to a leadership position in a GEM career. Furthermore, our study demonstrates that GEM graduates report high levels of career and clinical satisfaction.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".