Comparing the Maslach Burnout Inventory to Other Well-Being Instruments in Emergency Medicine Residents
Bibliographic record
Abstract
ABSTRACT Background The Maslach Burnout Inventory (MBI) is considered the “gold standard” for measuring burnout, encompassing 3 scales: emotional exhaustion, depersonalization, and personal accomplishment. Other well-being instruments have shown utility in various settings, and correlations between MBI and these instruments could provide evidence of relationships among key variables to guide well-being efforts. Objective We explored correlations between the MBI and other well-being instruments. Methods We fielded a multicenter survey of 9 emergency medicine (EM) residencies, administering the MBI and 4 published well-being instruments: a quality-of-life assessment, a work-life balance rating, an appraisal of career satisfaction, and the Primary Care Evaluation of Mental Disorders Patient Health Questionnaire 2 question screen. Consistent with the Maslach definition, burnout was defined by high emotional exhaustion (> 26) and high depersonalization (> 12). Results Of 334 residents, 261 (78%) responded. Residents who reported lower quality of life had higher emotional exhaustion (ρ = –0.437, P < .0001), higher depersonalization (ρ = –0.18, P < .005), and lower personal accomplishment (ρ = 0.347, P < .001). Residents who reported a negative work-life balance had emotional exhaustion (P < .001) and depersonalization (P < .009). Positive career satisfaction was associated with lower emotional exhaustion (P < .0001), lower depersonalization (P < .005), and higher personal accomplishment (P < .05). A positive depression screen was associated with higher emotional exhaustion, higher depersonalization, and lower personal achievement (all P < .0001). Conclusions Our multicenter study of EM residents demonstrated that assessments using the MBI correlate with other well-being instruments.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".