Predictors of burnout among physicians and advanced-practice clinicians in central New York
Bibliographic record
Abstract
Background: Provider wellbeing is a barometer of the strength of healthcare systems/organizations. Burnout prevalence among physicians exceeds that among other adult workers in the United States. Rural-based practitioners might be at greater risk.Objective: We investigated predictors of burnout among group employed providers within an integrated healthcare network.Methods: In a prospective observational study of physicians/advanced-practice clinicians serving an 8-county region of central New York, we linked administrative practice-setting data with responses to a questionnaire-survey comprising validated measures of burnout, resilience, work meaningfulness, satisfaction, risk aversion, and uncertainty/ambiguity tolerance. We included providers on the official payroll, excepting advisory board and/or research team members plus those who retired, resigned or were fired. 308 (65.1%) of 473 eligible clinicians completed the survey. 59.1% of these were physicians/doctoral-level practitioners; 40.9% advanced-practice clinicians. We assessed burnout using a validated 5-level single-item measure formatted as a binary outcome of “burned out/burning out” (levels 3–5) versus not. We derived a parsimonious generalized linear mixed-effects regression of this outcome on provider demographics, work-related needs, risk aversion, satisfaction, and unit characteristics.Results: Perceived workload, relatedness needs, practice satisfaction 75% of the time, dissatisfaction 50%, resilience, and practicing on a small unit were the significant, independent predictors.Conclusions: Heavy workloads, unmet relational needs, frequent dissatisfaction, low resilience, and serving on a small unit were most significantly associated with being “burned out/burning out”. Feeling satisfied most of the time and high resilience were protective. Profession, specialty, autonomy, and support staffing were not statistically significant.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".