Pediatrician Burnout Before and After the COVID-19 Pandemic
Bibliographic record
Abstract
Objective: During the surge of the COVID-19 pandemic, burnout among physicians increased significantly. In the spring of 2023, the COVID national emergency was terminated in the U.S. To investigate whether provider burnout rates have returned to pre-pandemic levels, the current study compared dimensions of burnout among pediatricians pre- and post-pandemic. Method: As part of 2 separate behavioral health trainings held at a Midwest academic health center in 2019 and virtually in 2023, data on burnout was collected from 52 pediatricians pre-pandemic and 38 pediatricians post-pandemic. Participants completed an online survey during the trainings and responded to items reflecting 3 dimensions of burnout: emotional exhaustion, depersonalization, and personal accomplishment. Results: There were no statistically significant differences in pre- and post-pandemic burnout amongst pediatricians in terms of total scores, number of pediatricians who met the clinical cutoff for each dimension, number of cutoffs met, or number of providers reporting elevated burnout on at least 1 dimension ( p > .05 for all comparisons). Participants were 1.77 times more likely to meet the cutoff for emotional exhaustion post-pandemic than pre-pandemic. Over half of providers met this cutoff post-pandemic, compared to only 35% pre-pandemic. Conclusions: While post-pandemic rates of burnout among pediatricians appear to be statistically similar to pre-pandemic levels, there appear to be clinically significant differences in emotional exhaustion between groups. With 63% of the post-pandemic group meeting the cutoff score for at least 1 dimension, it is imperative for the healthcare system to consider ways to mitigate burnout.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".