“The Cost in the Individual”
Bibliographic record
Abstract
OBJECTIVES: Emergency medicine (EM) confers a high risk of burnout that may be exacerbated by the COVID-19 pandemic. We aimed to determine the longitudinal prevalence of burnout in pediatric EM (PEM) physicians/fellows working in tertiary PEM departments across Canada and its fluctuation during the pandemic. METHODS: A national mixed-methods survey using a validated 2-question proxy for burnout was distributed monthly through 9 months. The primary outcome was the trajectory in probability of burnout, which was examined as both emotional exhaustion (EE) and depersonalization (DP), EE alone, and DP alone. Secondary outcomes investigated burnout and its association with demographic variables. Quantitative data were analyzed using logistic regression for primary outcomes and subanalyses for secondary outcomes. Conventional content analysis was used to analyze qualitative data and generate themes. RESULTS: From February to October 2021, 92 of 98 respondents completed at least 1 survey, 78% completed at least 3 consecutive surveys, and 48% completed at least 6 consecutive surveys. Predicted probability of EE was bimodal with peaks in May (25%) and October (22%) 2021. Rates of DP alone or having both EE and DP were approximately 1% and stable over the study period. Mid-career physicians were at lower risk of EE (odds ratio, 0.02; 95% confidence interval, 0-0.22) compared with early-career physicians. Underlying drivers of burnout were multifaceted. CONCLUSIONS: Our study suggests that increased COVID-19 case burden was correlated with EE levels during the third and fourth waves of the pandemic. Emotional exhaustion was worsened by systemic factors, and interventions must target common themes of unsustainable workloads and overwhelming lack of control.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".