Faculty development using a virtual community of practice: Three‐year outcomes of the Academic Life in Emergency Medicine Faculty Incubator program
Bibliographic record
Abstract
INTRODUCTION: The Academic Life in Emergency Medicine (ALiEM) Faculty Incubator program is a longitudinal, 1-year, virtual faculty development program for early- and mid-career faculty members that crosses specialties and institutions. This study sought to evaluate the outcomes among 3 years of participants. METHODS: This cross-sectional survey study evaluated postcourse and 1-year outcomes from three graduated classes of the ALiEM Faculty Incubator program. The program evaluation survey was designed to collect outcomes across multiple Kirkpatrick levels using pre/post surveys and tracking of abstracts, publications, speaking opportunities, new leadership positions, and new curricula. RESULTS: Over 3 years, 89 clinician educators participated in the program. Of those, 59 (66%) completed the initial survey and 33 (37%) completed the 1-year survey. Participants reported a significant increase in knowledge (4.1/9.0 vs. 7.0/9.0). The number of abstracts, publications, and invited presentations significantly increased after course completion and continued postcourse. A total of 37 of 59 (62.7%) developed a new curriculum during the course and 19 of 33 (57.6%) developed another new curriculum after the course. A total of 29 of 59 (49.2%) began a new leadership position upon course completion with 15 of 33 (45.5%) beginning another new leadership position 1 year later. DISCUSSION: The ALiEM Faculty Incubator program demonstrated an increase in perceived knowledge and documented academic productivity among early- and mid-career medical educators.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".