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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".