High Burden of Burnout on Rheumatology Practitioners
Bibliographic record
Abstract
Objective Burnout among physicians is common and has important implications. We assessed the extent of burnout among rheumatology practitioners and its associations. Methods. One hundred twenty-eight attendees at the 2019 Rheumatology Winter Clinical Symposium were surveyed using the Maslach Burnout Index (MBI) and a demographics questionnaire. Scores for emotional exhaustion (EE) ≥ 27, depersonalization (DP) ≥ 10, and personal accomplishment (PA) ≤ 33 were considered positive for burnout. Data regarding practitioner characteristics including age, sex, years in practice, and other demographics of interest were also collected. These data were used to determine prevalence and interactions of interest between practitioner characteristics and the risk of burnout. Results. Of the 128 respondents, 50.8% demonstrated burnout in at least 1 MBI domain. Dissatisfaction with electronic health records was associated with a 2.86-times increased likelihood of burnout (OR 2.86, 95% CI 1.23–6.65, P = 0.015). Similar results were found for lack of exercise (OR 5.00, 95% CI 1.3–18.5, P = 0.016) and work hours > 60 per week (OR 2.6, 95% CI 1.16–5.6, P = 0.019). Practitioners in group practice were 57% less likely to burn out (OR 0.43, 95% CI 0.20–0.92, P = 0.029), as were those who spend > 20% of their time in personally satisfying work (OR 0.32, 95% CI 0.15–0.71, P = 0.005). Conclusion. In what we believe to be one of the largest studies regarding burnout among rheumatology practitioners, we found a substantial prevalence of burnout, with 51% of all respondents meeting criteria in at least 1 domain defined by the MBI and 54% of physicians meeting these same criteria.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".