P166: The chief resident incubator - a virtual community of practice
Bibliographic record
Abstract
Introduction: The Emergency Medicine Chief Resident Incubator is a year-long curriculum for chief residents that aims to provide participants with a virtual community of practice, formal administrative training, mentorship, and opportunities for scholarship. Methods: The Chief Resident Incubator was designed by Academic Life in Emergency Medicine (ALiEM; www.aliem.com ) a digital health professions education organization in 2015, following a needs assessment in emergency medicine. A 12-month curriculum was created using constructivist social learning theory, with specific learning objectives that reflected 11 key administrative or professional development domains deemed important to chief residents. The topics covered included interviewing skills, contract negotiations, leadership, coaching, branding, conflict resolution, and ended with a focus on wellness and career longevity. A Core Leadership Team and Virtual Mentors were recruited to lead each annual iteration of the curriculum. The Incubator was implemented as a virtual community of practice using Slack©, a messaging and digital communication platform. Ancillary technology such as Google Hangout on Air© and Mailchimp© were used to facilitate learner engagement with the curriculum. Three in person networking events were hosted at three large emergency medicine and education conferences with special medical education guests. Outcomes include chief resident participation rates, Slack© activity, Google Hangout© web analytics, newsletter email engagement, and scholarship. We also incorporated a hidden curriculum throughout the year with multiple online publications, competitions for guest grand round presentations, and incorporation of digital technologies in medical education. Results: A total of 584 chief residents have participated over the first 3 years of the Chief Resident Incubator; this includes chief residents from over 212 residency programs across North America. Over 27,000 messages have been shared on Slack© (median 214 per week). A total of 32 Google Hangouts© have occurred over the course of the inaugural Incubator including faculty mentorship from Dr. Rob Rogers, Dr. Dara Kass and Dr. Amal Mattu. A monthly newsletter was distributed to the participants with an opening rate of 59%. Scholarship included 26 published academic blog posts, 2 open access In-Training exam prepbooks, a senior level online curriculum with 9 published modules and 3 book club reviews. Conclusion: The Chief Resident Incubator is a virtual community of practice that provides longitudinal training and mentorship for chief residents. This Incubator framework may be used to design similar professional development curricula across various health professions using an online digital platform.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.075 | 0.012 |
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".