Impact of a Wellness Leadership Intervention on the Empathy, Burnout, and Resting Heart Rate of Medical Faculty
Bibliographic record
Abstract
Objective To evaluate the efficacy of a wellness leadership intervention for improving the empathy, burnout, and physiological stress of medical faculty leaders. Participants and Methods Participants were 49 medical faculty leaders (80% physicians, 20% basic scientists; 67% female). The 6-week course was evaluated with a 15-week longitudinal waitlist-control quasi-experiment from September 1, 2021, through December 20, 2021 (during the COVID-19 pandemic). We analyzed 3 pretest-posttest-posttest and 6 weekly survey measurements of affective empathy and burnout, and mean=85 (SD=31) aggregated daily resting heart rates per participant, using 2-level hierarchical linear modeling. Results The course found a preventive effect for leaders' burnout escalation. As the control group awaited the course, their empathy decreased (coefficient Time =−1.27; P =.02) and their resting heart rates increased an average of 1.4 beats/min (coefficient Time =0.18; P <.001), reflecting the toll of the pandemic. Intervention group leaders reported no empathy decrements (coefficient Time =.33; P =.59) or escalated resting heart rate (coefficient Time =−0.05; P =.27) during the same period. Dose-response analysis revealed that both groups reduced their self-rated burnout over the 6 weeks of the course (coefficient Time =−0.28; P =.007), and those who attended more of the course showed less heart rate increase (coefficient Time∗Dosage =−0.05; P <.001). In addition, 12.73% of the within-person fluctuation in empathy was associated with burnout and resting heart rate. Conclusion A wellness leadership intervention helped prevent burnout escalation and empathy decrement in medical faculty leaders during the COVID-19 pandemic, showing potential to improve the supportiveness and psychological safety of the medical training environment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".