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Sexual Harassment, Abuse, and Discrimination in Obstetrics and Gynecology

2024· review· en· W4396733501 on OpenAlexaffabout
Ankita Gupta, Jennifer Thompson, Nancy E. Ringel, Shunaha Kim-Fine, Lindsay A. Ferguson, Stephanie V. Blank, Cheryl B. Iglesia, Ethan M. Balk, Angeles Alvarez Secord, Jeffrey F. Hines, Jubilee Brown, Cara L. Grimes

Bibliographic record

VenueJAMA Network Open · 2024
Typereview
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHarassmentObstetrics and gynaecologyObstetricsGynecologyMedicineSexual abusePsychologyMedical emergencyPregnancyPoison controlSuicide preventionNursingBiology

Abstract

fetched live from OpenAlex

Importance: Unlike other surgical specialties, obstetrics and gynecology (OB-GYN) has been predominantly female for the last decade. The association of this with gender bias and sexual harassment is not known. Objective: To systematically review the prevalence of sexual harassment, bullying, abuse, and discrimination among OB-GYN clinicians and trainees and interventions aimed at reducing harassment in OB-GYN and other surgical specialties. Evidence Review: A systematic search of PubMed, Embase, and ClinicalTrials.gov was conducted to identify studies published from inception through June 13, 2023.: For the prevalence of harassment, OB-GYN clinicians and trainees on OB-GYN rotations in all subspecialties in the US or Canada were included. Personal experiences of harassment (sexual harassment, bullying, abuse, and discrimination) by other health care personnel, event reporting, burnout and exit from medicine, fear of retaliation, and related outcomes were included. Interventions across all surgical specialties in any country to decrease incidence of harassment were also evaluated. Abstracts and potentially relevant full-text articles were double screened.: Eligible studies were extracted into standard forms. Risk of bias and certainty of evidence of included research were assessed. A meta-analysis was not performed owing to heterogeneity of outcomes. Findings: A total of 10 eligible studies among 5852 participants addressed prevalence and 12 eligible studies among 2906 participants addressed interventions. The prevalence of sexual harassment (range, 250 of 907 physicians [27.6%] to 181 of 255 female gynecologic oncologists [70.9%]), workplace discrimination (range, 142 of 249 gynecologic oncologists [57.0%] to 354 of 527 gynecologic oncologists [67.2%] among women; 138 of 358 gynecologic oncologists among males [38.5%]), and bullying (131 of 248 female gynecologic oncologists [52.8%]) was frequent among OB-GYN respondents. OB-GYN trainees commonly experienced sexual harassment (253 of 366 respondents [69.1%]), which included gender harassment, unwanted sexual attention, and sexual coercion. The proportion of OB-GYN clinicians who reported their sexual harassment to anyone ranged from 21 of 250 AAGL (formerly, the American Association of Gynecologic Laparoscopists) members (8.4%) to 32 of 256 gynecologic oncologists (12.5%) compared with 32.6% of OB-GYN trainees. Mistreatment during their OB-GYN rotation was indicated by 168 of 668 medical students surveyed (25.1%). Perpetrators of harassment included physicians (30.1%), other trainees (13.1%), and operating room staff (7.7%). Various interventions were used and studied, which were associated with improved recognition of bias and reporting (eg, implementation of a video- and discussion-based mistreatment program during a surgery clerkship was associated with a decrease in medical student mistreatment reports from 14 reports in previous year to 9 reports in the first year and 4 in the second year after implementation). However, no significant decrease in the frequency of sexual harassment was found with any intervention. Conclusions and Relevance: This study found high rates of harassment behaviors within OB-GYN. Interventions to limit these behaviors were not adequately studied, were limited mostly to medical students, and typically did not specifically address sexual or other forms of harassment.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.099
GPT teacher head0.396
Teacher spread0.296 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations13
Published2024
Admission routes2
Has abstractyes

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