The ALiEM Faculty Incubator: A Novel Online Approach to Faculty Development in Education Scholarship
Bibliographic record
Abstract
PROBLEM: Early- and midcareer clinician educators often lack a local discipline-specific community of practice (CoP) that encourages scholarly activity. As a result, these faculty members may feel disconnected from other scholars. APPROACH: Academic Life in Emergency Medicine (ALiEM) piloted the Faculty Incubator. This longitudinal, asynchronous, online curriculum focused on developing a virtual CoP among 30 early- to midcareer medical educators (the "incubatees"), 8 core faculty mentors, and 10 guest mentors. The yearlong curriculum included 12 monthly modules focusing on core concepts in medical education scholarship. The initiative connected the incubatees with a virtual community of peers and mentors, with whom they completed multiple scholarly projects, sought mentorship, and engaged professionally. The authors used an online, closed, social media platform (Slack) to facilitate the exchange of ideas. OUTCOMES: In the inaugural year (March 2016-February 2017), the mentorship team facilitated exceptional levels of online engagement among incubatees. All participants (incubatees, core mentors, and guest mentors) shared 1,081 files and exchanged a total of 22,665 messages (approximately 62 per day). Of these, 3,036 (13.4%) were via open channels, 5,483 (24.2%) via small groups, and 14,146 (62.4%) via direct messages. NEXT STEPS: The ALiEM Faculty Incubator represents a proof of concept, and initial outcomes show that it is possible to engage an international group of early- to midcareer medical educators to create a vibrant online CoP. The Faculty Incubator leaders plan to determine whether this engaged group of health professions educators will increase their scholarly output as a result of this initiative.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".