Executive Summary from the 2017Â Emergency Medicine Resident Wellness Consensus Summit
Bibliographic record
Abstract
\n Introduction: Physician wellness has recently become a popular topic of conversation and publication within the house of medicine and specifically within emergency medicine (EM). Through a joint collaboration involving Academic Life in Emergency Medicine’s (ALiEM) Wellness Think Tank, Essentials of Emergency Medicine (EEM), and the Emergency Medicine Residents’ Association (EMRA), a one-day Resident Wellness Consensus Summit (RWCS) was organized.\n \n \n Methods: The RWCS was held on May 15, 2017, as a pre-day event prior to the 2017 EEM conference in Las Vegas, Nevada. Seven months before the RWCS event, pre-work began in the ALiEM Wellness Think Tank, which was launched in October 2016. The Wellness Think Tank is a virtual community of practice involving EM residents from the U.S. and Canada, hosted on the Slack digital-messaging platform. A working group was formed for each of the four predetermined themes: wellness curriculum development; educator toolkit resources for specific wellness topics; programmatic innovations; and wellness-targeted technologies. \n \n \n Results: Pre-work for RWCS included 142 residents from 100 different training programs in the Wellness Think Tank. Participants in the actual RWCS event included 44 EM residents, five EM attendings who participated as facilitators, and three EM attendings who acted as participants. The four working groups ultimately reached a consensus on their specific objectives to improve resident wellness on both the individual and program level. \n \n \n Conclusion: The Resident Wellness Consensus Summit was a unique and novel consensus meeting, involving residents as the primary stakeholders. The summit demonstrated that it is possible to galvanize a large group of stakeholders in a relatively short time by creating robust trust, communication, and online learning networks to create resources that support resident wellness.
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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 | 0.026 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.128 | 0.064 |
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".