Burnout in Medical School Deans: An Uncommon Problem
Bibliographic record
Abstract
PURPOSE: To assess the burnout level among U.S. and Canadian medical school deans and to study how burnout relates to certain characteristics including hours worked, effectiveness, and support from family and colleagues. METHOD: Questionnaires were sent in September 2006 to 100 deans who had served at least one year. The questionnaire included 13 questions from the Maslach Burnout Inventory-Human Services Survey (MBI-HSS), which measures three subcomponents of burnout: emotional exhaustion, depersonalization, and personal accomplishment. RESULTS: The response rate was 90%. The median age of participants was 60; the median length of service at the current institution was four years; the median number of hours worked per week was 70.0. Deans most frequently identified school budget deficits, loss of funding, and departure of key faculty as stressors. Whereas only 11% reported being dissatisfied with their positions, 33% reported it was likely they would step down within the next two years. The predominant pattern of MBI-HSS subscale scores in participating deans was moderate emotional exhaustion, moderate depersonalization, and high personal accomplishment. Only 2% of respondents satisfied all three subscale scores for high burnout. Emotional exhaustion was significantly directly associated with work week length and number of weekend days worked and was inversely associated with spousal support, length of service, and age. CONCLUSIONS: Despite having an "extreme" job, only 2% of deans exhibited high levels of burnout. A high sense of control and self-efficacy, a supportive family, increasing length of service, and increasing age may be factors which reduce burnout in deans.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".