Not all call is created equally: The impact of culture and sex on burnout related to in-house call
Bibliographic record
Abstract
BACKGROUND: In-house call (IHC) has previously been shown to result in increased burnout in acute care surgeons (ACSs). There is wide variation, however, in the implementation and culture of work surrounding IHC across trauma centers and within the demographics of practicing ACSs. We hypothesized that local work practices and culture surrounding IHC as well as sex of ACSs would impact burnout. METHODS: Continuous physiologic data were collected over 6 months from 224 ACSs who wore a fitness wearable. Acute care surgeons were sent daily surveys to record work, personal activities, and feelings of burnout. The Maslach Burnout Inventory was completed by ACSs at the beginning and end of the study period. RESULTS: Forty-eight (21.5%) of ACS reported being expected to complete the usual workday after IHC, 94 (42.2%) were expected to finish work from IHC, and 81 (36.3%) were expected to leave immediately after IHC was over. Acute care surgeons expected to complete a usual workday postcall were more likely to be burned out, and IHC resulted in a greater increase in their daily feelings of burnout than among ACSs who reported working in other work cultures. Females showed higher levels of daily burnout than males but no difference in the degree to which IHC led to burnout. CONCLUSION: In-house call results in increased burnout in all ACSs; however, IHC had a larger impact on daily feelings of burnout in ACSs expected to work without adjustments to their work schedule postcall. Although female ACSs reported higher levels of daily burnout than male ACSs, IHC increased daily feelings of burnout equally between the two sexes. Taken together, these findings necessitate caution about work expectations surrounding IHC and suggest a need for the deliberate creation of a postcall culture for ACS. LEVEL OF EVIDENCE: Prognostic and Epidemiological; Level III.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".