Burnout Among Nephrologists in the United States: A Survey Study
Bibliographic record
Abstract
Rationale & Objective: Burnout decreases job satisfaction and leads to poor patient outcomes but remains underinvestigated in nephrology. We explored the prevalence and determinants of burnout among a sample of nephrologists. Study Design: Cross-sectional. Setting & Participants: The nephrologists were approached via the American Medical Association Physicians Masterfile, National Kidney Foundation listserv, email, and social media between April and August 2019. The predictors were demographics and practice characteristics. The outcome was burnout, defined as responding "once a week" or more on either 1 of the 2 validated measures of emotional exhaustion and depersonalization or both. Analytical Approach: tests. Multivariable logistic regression was used to estimate the odds ratios (ORs) of burnout for risk factors. Free text responses were thematically analyzed. Results: About half of 457 respondents were 40-59 years old (n=225; 49.2%), and the respondents were more predominantly men (n=296; 64.8%), US medical graduates (n=285; 62.4%), and in academic practice (n=286; 62.6%). Overall, 106 (23.2%) reported burnout. The most commonly reported primary drivers of burnout were the number of hours worked (n=27; 25.5%) and electronic health record requirements (n=26; 24.5%). Caring for ≤25 versus 26-75 patients per week (OR, 0.34; 95% confidence interval [95% CI], 0.15-0.77), practicing in academic versus nonacademic settings (OR, 0.33; 95% CI, 0.21-0.54), and spending time on other responsibilities versus patient care (OR, 0.32; 95% CI, 0.17-0.61) were each independently associated with nearly 70% lower odds of burnout after adjusting for age, sex, race, and international medical graduate status. The free text responses emphasized disinterested health care systems and dissatisfaction with remuneration as the drivers of burnout. Limitations: Inability to precisely capture response rate. Conclusions: Nearly one-quarter of the nephrologists in our sample reported burnout. Future studies should qualitatively investigate how the care setting, time spent on electronic medical records, and hours of clinical care drive burnout and explore other system-level drivers of burnout in nephrology.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".