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Record W4406018366 · doi:10.1213/ane.0000000000007282

Sexual Harassment in Academic Anesthesiology: A Survey of Prevalence, Sources, Impact, and Recommendations

2025· article· en· W4406018366 on OpenAlexaboutno aff
Maya Jalbout Hastie, Aaron Mittel, Vidya T. Raman, Joseph W. Szokol, Robert A. Whittington, Maria A. Bustillo, Shahla Siddiqui, Tracey Straker, Tetsuro Sakai, Valerie E. Armstead, Jeanine P. Wiener-Kronish, Chelcie Jewitt, George A. Mashour

Bibliographic record

VenueAnesthesia & Analgesia · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHarassmentAnesthesiologyFamily medicineSurvey researchMedical educationNursingAnesthesiaApplied psychology

Abstract

fetched live from OpenAlex

BACKGROUND: A report by the American Association of Medical Colleges (AAMC) showed that academic anesthesiology has the highest prevalence of sexual harassment among specialties for both men and women. We aimed to explore the prevalence, sources, and impact of sexual harassment on anesthesiologists in academic centers in the United States and Canada. We also sought recommendations for its mitigation. METHODS: An anonymous online survey instrument was designed based on a previously published report, yielding 39 questions, including demographics and 4 open-ended questions. The survey was sent via email to Association of University Anesthesiologists (AUA) members, who were encouraged to share across academic anesthesiology departments in the United States and Canada. RESULTS: A total of 626 responses were received; after exclusion of incomplete and nonfaculty responses, 484 complete survey responses were analyzed. 52.9% of respondents identified as men and 45.9% as women; 3 respondents (0.6%) identified as nonbinary, and 3 respondents (0.6%) preferred not to answer. 43.6% of respondents perceived there is sexual harassment in academic anesthesiology. Significantly more women than men reported presence of sexual harassment in academic medicine (65.3% vs 38.3%, P < .001), in academic anesthesiology (59.5% vs 30.1%, P < .001), and in their place of work (37.8% vs 18.3%, P < .001). 14.5% of men and 43.2% of women had experienced sexual harassment at least once in the past 12 months ( P < .001). 43.7% of women reported ever experiencing unwanted physical contact in the workplace compared to 16.8% of men; 74.3% of women reported ever experiencing verbal or nonverbal conduct in the workplace related to gender that caused embarrassment, distress, or offense compared to 24.6% of men ( P < .001). 8.2% of men reported feeling their clinical ability doubted, compared to 87.8% of women ( P < .001). Experiences of sexual harassment were most consistent with verbal and nonverbal behaviors that convey hostility, objectification, or exclusion of members of one gender. Colleagues from anesthesiology were most likely to be reported as the source of sexual harassment (44.6% of unwanted physical contact, 59% of verbal or nonverbal conduct). The impact was described along 4 themes: emotional, cognitive, behavioral, and professional. Participants made recommendations for eliminating sexual harassment by raising awareness, providing education, establishing reporting, offering support, and ensuring accountability. CONCLUSIONS: This survey confirms the high prevalence of sexual harassment in academic anesthesiology. The most common sources are anesthesiology colleagues. The recommendations for leaders and institutions include creating a professional environment free from harassment with support for targets and accountability for instigators.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.997
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.038
GPT teacher head0.357
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
DomainIncentives
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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