Faculty burnout in a large department of pediatrics
Bibliographic record
Abstract
Background: Physician stress is at a heightened level in the midst of a changing health care environment and limited research funding conditions. Burnout of physicians has an impact on their personal health, related institutional health care costs, retention of quality faculty, and patient care and safety. The present study was designed to assess levels of burnout and sources of stress in clinical, educational, and research faculty working in a Department of Pediatrics and a large Children’s Hospital setting.Methods: The self-report Maslach Burnout Inventory was given to the 240 faculty members in a large Department of Pediatrics based at a Children’s Hospital as part of a larger faculty development needs survey designed to inform faculty development programming. Participation in the survey was voluntarily, and was presented during a mandatory faculty meeting and then emailed to all faculty so those who were unable to attend could participate. A brief survey on sources of stress, stress-related illness, and wellness programming needs was also administered.Results: Out of the 240 faculty members, 86 surveys were collected and analyzed (36% response rate). 52% were female, 84% were Caucasian. Rank of respondents appeared fairly consistent with overall Departmental distribution (40% Assistant, 22% Associate, 34% Full, 4% Instructor). 48% of respondents were Clinical Educators, with 18% Basic Scientists, 17% Clinical Attendings, and 16% Clinical Scientists, also fairly consistent with overall Departmental distributions. Results suggest 65% of faculty endorse high levels of emotional exhaustion, 56% high levels of depersonalization, and 100% felt low levels of personal accomplishment. According to Maslach Burnout Inventory (MBI) cutoff criteria, 94% of respondents were above the cutoff for Burnout, much higher than recent, published national norms. The most frequently identified top stressors were: lack of connectedness to the institution, administrative responsibilities, time conflicts, clinical workload and general workload. The most frequently identified top stress symptoms experienced were: mood issues, health-related behaviors, and psychosomatic symptoms.Conclusions: Burnout rates at this institution are higher than national norms. From the data collected, faculty development programs to address coping with burnout and prevention such as strategies to promote resilience to stress will be provided. Faculty will be periodically provided with currently available sources of individual and group support and these will be easily available on the internal website. Institutional changes that may affect burnout rates should also be considered.
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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 | 0.002 | 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 | 0.000 |
| 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".