Curriculum Design and Implementation of the Emergency Medicine Chief Resident Incubator
Bibliographic record
Abstract
Background Chief residents receive minimal formal training in preparation for their administrative responsibilities. There is a lack of professional development programs specifically designed for chief residents. Objective In 2015, Academic Life in Emergency Medicine designed and implemented an annual, year-long, training program and virtual community of practice for chief residents in emergency medicine (EM). This study describes the curriculum design process and reports measures of learner engagement during the first two cycles of the curriculum. Methods Kern's Six-Step Approach for curriculum development informed key decisions in the design and implementation of the Chief Resident Incubator. The resultant curriculum was created using constructivist social learning theory, with specific objectives that emphasized the needs for a virtual community of practice, longitudinal content delivery, mentorship for participants, and the facilitation of multicenter digital scholarship. The 12-month curriculum included 11 key administrative or professional development domains, delivered using a combination of digital communications platforms. Primary outcomes measures included markers of learner engagement with the online curriculum, recognized as modified Kirkpatrick Level One outcomes for digital learning. Results An average of 206 chief residents annually enrolled in the first two years of the curriculum, with an overall participation by 33% (75/227) of the allopathic EM residency programs in the United States (U.S.). There was a high level of learner engagement, with an average 13,414 messages posted per year. There were also 42 small group teaching sessions held online, which included 39 faculty and 149 chief residents. The monthly e-newsletter had a 50.7% open rate. Digital scholarship totaled 23 online publications in two years, with 67 chief resident co-authors and 21 faculty co-authors. Conclusions The Chief Resident Incubator is a virtual community of practice that provides longitudinal training and mentorship for EM chief residents. This incubator conceptual 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 | 0.009 | 0.011 |
| 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 | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".