“Ardor and diligence”: Quantifying the faculty effort needed in emergency medicine graduate medical education
Bibliographic record
Abstract
Abstract Objectives Regulatory requirements around protected faculty effort to support graduate medical education (GME) programs have changed. The amount of labor required to run a GME program is unknown. We sought to describe the work performed by program leadership and core faculty in emergency medicine (EM). Methods We performed a prospective survey study of core faculty in EM. Participants completed a demographic questionnaire followed by quarterly time surveys, covering activities in eight domains: evaluation, teaching and education, scholarly activity, service, interview/recruitment, clinical supervision, student responsibilities, and wellness and administration. We collected data from April 2022 to March 2023. We calculated descriptive statistics and used analyses of variance (ANOVA) to assess differences by faculty role and quarter. Results A total of 596 physicians completed the demographic questionnaire and 347 (58.2%) completed at least one quarterly time survey including 142 (41%) females, 48 (14%) program directors (PDs), 84 (24%) assistant/associate program directors (APDs), and 215 (62%) general core faculty (GCF). The mean number of hours per week spent on nonclinical education work was 60 h for PDs, 47 h for APDs, and 44 h for GCF. ANOVA found significant differences in mean hours per week and faculty role in domains of evaluation ( p < 0.001), service ( p = 0.007), and interview/recruitment ( p < 0.001). We detected differences in mean hours per week and quarter in domains of evaluation ( p < 0.001), teaching and education ( p < 0.001), interview and recruitment ( p < 0.001), and clinical supervision ( p < 0.001). Conclusions Running a residency program requires many hours of faculty work, which can vary based on faculty role and time of year. These results can inform decisions regarding faculty support.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".