An Evidence-based, Longitudinal Curriculum for Resident Physician Wellness: The 2017 Resident Wellness Consensus Summit
Bibliographic record
Abstract
INTRODUCTION: Physicians are at much higher risk for burnout, depression, and suicide than their non-medical peers. One of the working groups from the May 2017 Resident Wellness Consensus Summit (RWCS) addressed this issue through the development of a longitudinal residency curriculum to address resident wellness and burnout. METHODS: A 30-person (27 residents, three attending physicians) Wellness Curriculum Development workgroup developed the curriculum in two phases. In the first phase, the workgroup worked asynchronously in the Wellness Think Tank - an online resident community - conducting a literature review to identify 10 core topics. In the second phase, the workgroup expanded to include residents outside the Wellness Think Tank at the live RWCS event to identify gaps in the curriculum. This resulted in an additional seven core topics. RESULTS: Seventeen foundational topics served as the framework for the longitudinal resident wellness curriculum. The curriculum includes a two-module introduction to wellness; a seven-module "Self-Care Series" focusing on the appropriate structure of wellness activities and everyday necessities that promote physician wellness; a two-module section on physician suicide and self-help; a four-module "Clinical Care Series" focusing on delivering bad news, navigating difficult patient encounters, dealing with difficult consultants and staff members, and debriefing traumatic events in the emergency department; wellness in the workplace; and dealing with medical errors and shame. CONCLUSION: The resident wellness curriculum, derived from an evidence-based approach and input of residents from the Wellness Think Tank and the RWCS event, provides a guiding framework for residency programs in emergency medicine and potentially other specialties to improve physician wellness and promote a culture of wellness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".