Perceptions of the current and future emergency medicine workforce
Bibliographic record
Abstract
Objective: and to examine how the workforce report may have influenced perceptions about job prospects. Methods: A cross-sectional survey was conducted in 2022 of EM residents, fellows, and attendings at 21 practice sites. Main outcomes were perceptions of the likelihood of currently finding any job, currently finding a desirable job, and confidence in the future EM job market. Results: Note that 831 of 1938 physicians (42.9%) responded. A total of 92.4% reported a high likelihood of finding any job currently, 49.8% reported a high likelihood of finding a desirable job currently, and 44.4% reported future confidence. Workforce report familiarity was associated with greater likelihood of finding a desirable job. Fellows were least confident in the future. Residents with desired Midwest location were twice as confident in the future job market; those with desired West location were less confident. Attendings 20 or more years post-training were more than twice as likely to report a high likelihood of finding a desirable job and almost twice as likely to report future confidence. Attendings in leadership were nearly three times as likely to report high a likelihood of finding a desirable job and future confidence. Conclusion: EM trainees and attendings have favorable perceptions of the current job market but are less confident in future prospects. As the projected surplus of EM physicians appears to have had an impact, updated projections are needed for more accurate assessments of the future of the specialty.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".