Impact of the intersection of anaesthesia and gender on burnout and mental health, illustrated by the COVID‐19 pandemic
Bibliographic record
Abstract
Physician burnout and poor mental health are prevalent and often stigmatised. Anaesthetists may be at particular risk and this is further increased for women anaesthetists due to biases and inequities within the specialty. However, gender-related risk factors for and experiences of burnout and poor mental health remain under-researched and under-reported. This negatively impacts individual practitioners, the anaesthesia workforce and patients and carries significant financial implications. We discuss the impact of anaesthesia and gender on burnout and mental health using the COVID-19 pandemic as an example illustrating how women and men differentially experience stressors and burnout. COVID-19 has further accentuated the gendered effects of burnout and poor mental health on anaesthetists and brought further urgency to the need to address these issues. While both personal and organisational factors contribute to burnout and poor mental health, organisational changes that recognise and acknowledge inequities are pivotal to bolster physician mental health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".