Identifying Gaps and Launching Resident Wellness Initiatives: The 2017 Resident Wellness Consensus Summit
Bibliographic record
Abstract
INTRODUCTION: Burnout, depression, and suicidality among residents of all specialties have become a critical focus for the medical education community, especially among learners in graduate medical education. In 2017 the Accreditation Council for Graduate Medical Education (ACGME) updated the Common Program Requirements to focus more on resident wellbeing. To address this issue, one working group from the 2017 Resident Wellness Consensus Summit (RWCS) focused on wellness program innovations and initiatives in emergency medicine (EM) residency programs. METHODS: Over a seven-month period leading up to the RWCS event, the Programmatic Initiatives workgroup convened virtually in the Wellness Think Tank, an online, resident community consisting of 142 residents from 100 EM residencies in North America. A 15-person subgroup (13 residents, two faculty facilitators) met at the RWCS to develop a public, central repository of initiatives for programs, as well as tools to assist programs in identifying gaps in their overarching wellness programs. RESULTS: An online submission form and central database of wellness initiatives were created and accessible to the public. Wellness Think Tank members collected an initial 36 submissions for the database by the time of the RWCS event. Based on general workplace, needs-assessment tools on employee wellbeing and Kern's model for curriculum development, a resident-based needs-assessment survey and an implementation worksheet were created to assist residency programs in wellness program development. CONCLUSION: The Programmatic Initiatives workgroup from the resident-driven RWCS event created tools to assist EM residency programs in identifying existing initiatives and gaps in their wellness programs to meet the ACGME's expanded focus on resident wellbeing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 teacher head, 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".