ALiEM Connect: Large-Scale, Interactive, Virtual Residency Programming in Response to COVID-19
Bibliographic record
Abstract
Problem: The COVID-19 pandemic restricted in-person gatherings, including residency conferences. The pressure to quickly reorganize educational conferences and convert content to a remote format overwhelmed many programs. This article describes the pilot event of a large-scale, interactive, virtual educational conference modeled, designed, and implemented by Academic Life in Emergency Medicine (ALiEM), called ALiEM Connect. Approach: The pilot ALiEM Connect event was conceptualized and implemented within a 2-week period in March 2020. The pilot was livestreamed via a combination of Zoom and YouTube and was archived by YouTube. Slack was used as a backchannel to allow interaction with other participants and engagement with the speakers (via moderators who posed questions from the backchannel to the speakers live during the videoconference). Outcomes: The RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework was used for program evaluation, showing that 64 U.S. Accreditation Council for Graduate Medical Education-accredited emergency medicine residency programs participated in the pilot event, with 1,178 unique users during the event (reach). For effectiveness, 93% (139/149) of trainees reported the pilot as enjoyable and 85% (126/149) reported it was equivalent to or better than their usual academic proceedings. Adoption for ALiEM Connect was fairly good with 64/237 (27%) of invited residency programs registering and participating in the pilot event. Implementation was demonstrated by nearly half of the livestream viewers (47%, 553/1,178) interacting in the backchannel discussion, sending a total of 4,128 messages in the first 4 hours. Next steps: The final component of the RE-AIM framework, maintenance, will take more time to evaluate. Further study is required to measure the educational impact of events like the ALiEM Connect pilot. The ALiEM Connect model could potentially be used to replace educational conferences that have been canceled or to implement and/or augment a large-scale, shared curriculum among residency programs in the future.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.005 |
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".