Stress From Uncertainty and Resilience Among Depressed and Burnt Out Residents: Cross-Sectional Study
Bibliographic record
Abstract
Objective: To determine how stress from uncertainty is related to resilience among pediatric residents and whether these attributes are associated with depression and burnout.\n\nStudy Design and Setting: Cross-sectional study of 50 residents in pediatric residency programs from four urban freestanding children’s hospitals in the United States and Canada.\n\nMain outcome measures: Stress from uncertainty using the Physicians’ Reaction to Uncertainty Scale, resilience using the 14-item Resilience Scale, depression using the Harvard national depression screening scale, and burnout using single item measures of emotional exhaustion and depersonalization from the Maslach Burnout Inventory.\n\nResults: There was a strong correlation between stress from uncertainty and resilience (r=-0.597; p<0.00001). 5 residents (10%) met the criteria for depression and 15 residents (31%) met the criteria for high burnout. Depressed residents were more likely to be stressed by uncertainty (mean 51.6; SD 9.07 vs. mean 38.7; SD 6.73; p=0.0003) and to lack resilience (mean 56.6; SD 10.7 vs. mean 85.4; SD 7.97; p<0.0001) compared to residents who were not depressed. Burnt out residents were also more likely to be stressed by uncertainty (mean 44; SD 8.46 vs. mean 38.3; SD 7.13; p=0.0186) and to lack resilience (mean 76.7; SD 14.8 vs. mean 85.0; SD 9,77; p=0.0242) compared to residents who were not burnt out. We were able to identify the scores at which stress from uncertainty best predicts depression and burnout.\n\nConclusion: Depression and burnout are major problems among pediatric residents. We found strong correlations between stress from uncertainty, resilience, depression, and burnout. Efforts to enhance tolerance of uncertainty and resilience among residents may provide opportunities to mitigate resident depression and 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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".