Burnout among Academic Clinicians as It Correlates with Workload and Demographic Variables
Bibliographic record
Abstract
Burnout syndrome (BOS) in academic physicians is a psychological state resulting from prolonged exposure to job stressors. It leads to a decline in overall job performance, which could result in misjudgment and serious clinical errors. The current study identifies the prevalence, as well as the potential demographic and workload variables that contribute significantly to BOS in academic clinicians. We distributed a modified version of the Maslach Burnout Inventory (MBI) scale to the academic clinicians in our institution; 326/900 responded, with 56.21% male and 43.46% female. The MBI scale comprised of three dimensions of burnout: emotional exhaustion (EE), depersonalization (DP), and personal accomplishment (PA). Higher scores in EE and DP and lower scores in PA were associated with a higher risk for burnout. In considering the work-life of academic clinicians, this study used a modified version of the MBI to reflect three hypothesized sources of burnout: interactions with students/trainees, interactions with patients, and interactions with administration, as reflected in these three dimensions. Along both the EE and DP dimensions of the MBI, burnout was highest for interactions with administration (51% and 44.8%), moderate for interactions with patients (26.4% and 34.5%), and lowest for interactions with students (11.7% and 9.8%). The highest scores along the personal accomplishment component was found for interactions with students and patients (33.7% and 33.4%). Regression analyses identified several factors associated with higher scores on the EE and DP scales: younger age, surgical specialty, low academic rank, academic main practice, female gender, numerous night shifts, and living alone. Furthermore, higher patient volume contributed significantly to the increasing PA. This study suggests that administrative interaction contributes significantly to burnout amongst physicians, followed by patient care and trainees. Furthermore, surgeons, females, single, early career, and younger faculty staff members are at higher risk of suffering from burnout. Further studies are needed to characterize the nature of administrative interactions that contribute to burnout and to solidify other contributing variables.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| 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".