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 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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".