Addressing Rheumatology Resident Well-Being Is Critical to the Rheumatology Workforce and the Care of Our Patients
Bibliographic record
Abstract
Matriculating medical students have lower levels of burnout compared to age-matched college graduates,1 and yet residents and practicing physicians have higher rates of burnout compared to the general population,2,3 suggesting that medical training may play a role in increasing rates of burnout. Burnout, characterized by Maslach and Jackson as the triad of depersonalization, emotional exhaustion, and loss of a sense of personal accomplishment,4 is well documented in physicians around the world and has been shown to be increasing over time, particularly after the start of the coronavirus disease 2019 (COVID-19) pandemic.5,6 There is a lack of longitudinal data on the effect of the pandemic on rheumatologists specifically, although in a survey of Canadian rheumatologists conducted between 2020 and 2021 during the COVID-19 pandemic, 51% of rheumatologists met the criteria for burnout.7 A recent Medscape survey reported that rheumatologists have the second-highest burnout levels among 29 specialties.8 In this issue of The Journal of Rheumatology , McGoldrick et al9 evaluated burnout among American rheumatology fellows in a cross-sectional study performed in 2019. One hundred five fellows completed a survey designed to evaluate burnout using an adapted version of the previously validated Maslach Burnout Inventory.10 The authors also measured secondary outcomes such as depression, quality of life, and fatigue.9 They posed open-ended questions to collect qualitative data on perceived factors that reduce and factors that worsen burnout. McGoldrick et al found that 38.5% of postgraduate year (PGY) 4 and 16.7% of PGY5/6 fellows had at least 1 symptom of burnout.9 PGY4 fellows had higher rates of burnout, more depression, worse fatigue, and worse quality of life compared to PGY5 fellows. Younger age was associated with higher levels of burnout, but there was no variation by gender, race/ethnicity, or … Address correspondence to Dr. D.C. Jerome, Women’s College Hospital, 76 Grenville Street, Toronto, ON M5S 1B2, Canada.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".