An Evaluation of Burnout Among US Rheumatology Fellows: A National Survey
Bibliographic record
Abstract
OBJECTIVE: To evaluate levels of burnout and correlates of burnout among US rheumatology fellows. METHODS: US rheumatology fellows were invited to complete an electronic survey in 2019. Burnout was assessed using the Maslach Burnout Inventory. Measures of depression, fatigue, quality of life, and training year were also collected. Open-ended questions about perceived factors to promote resiliency and factors leading to increased burnout were included. Bivariate and multivariate regression analyses were used to examine correlates of burnout. Open-ended responses were analyzed using thematic analysis. RESULTS: The response rate was 18% (105/582 pediatric and adult rheumatology fellows). Over one-third (38.5%) of postgraduate year (PGY) 4 and 16.7% of PGY5/6 fellows reported at least 1 symptom of burnout. Of PGY4 fellows, 12.8% met criteria for depression compared with 2.4% of PGY5/6 fellows. PGY4 fellows reported worse fatigue and poorer quality of life compared with PGY5/6. In multivariable models controlling for training year and gender, older age (> 31 years) was associated with lower odds of burnout. Thematic analysis of open-ended responses identified factors that help reduce burnout: exercise, family/friends, sleep, support at work, and hobbies. Factors contributing to burnout: pager, documentation, long hours, demands of patient care, and presentations and expectations. CONCLUSION: This national survey of US rheumatology fellows reveals that early trainee level and younger age are associated with worse levels of fatigue, quality of life, and burnout. Although awareness of and strategies to reduce burnout are needed for all fellows, targeted interventions for younger fellows and those in their first year of training may be of highest yield.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".