CAEP 2016 Academic Symposium on Education Scholarship: Training our Future Clinician Educators in Emergency Medicine
Bibliographic record
Abstract
OBJECTIVE: To develop consensus recommendations for training future clinician educators (CEs) in emergency medicine (EM). METHODS: A panel of EM education leaders was assembled from across Canada and met regularly by teleconference over the course of 1 year. Recommendations for CE training were drafted based on the panel's experience, a literature review, and a survey of current and past EM education leaders in Canada. Feedback was sought from attendees at the Canadian Association of Emergency Physicians (CAEP) annual academic symposium. Recommendations were distributed to the society's Academic Section for further feedback and updated by a consensus of the expert panel. RESULTS: Recommendations were categorized for one of three audiences: 1) Future CEs; 2) Academic departments and divisions (AD&D) that support training to fulfill their education leadership goals; and 3) The CAEP Academic Section. Advanced medical education training is recommended for any emergency physician or resident who pursues an education leadership role. Individuals should seek out mentorship in making decisions about career opportunities and training options. AD&D should regularly perform a needs assessment of their future CE needs and identify and encourage potential individuals who fulfill education leadership roles. AD&D should develop training opportunities at their institution, provide support to complete this training, and advocate for the recognition of education scholarship in their institutional promotions process. The CAEP Academic Section should support mentorship of future CEs on a national scale. CONCLUSION: These recommendations serve as a framework for training and supporting the next generation of Canadian EM 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 | 0.049 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".