MétaCan
Menu
Back to cohort
Record W4388213768 · doi:10.2106/jbjs.oa.23.00067

Gender Representation in Major Orthopaedic Surgery Meetings

2023· review· en· W4388213768 on OpenAlexaffabout
Prushoth Vivekanantha, A H Dao, Laurie A. Hiemstra, Maegan N. Shields, Andrea Chan, Veronica Wadey, Peter C. Ferguson, Ajay Shah

Bibliographic record

VenueJBJS Open Access · 2023
Typereview
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsBanff CentreUniversity of CalgaryQueen's UniversityUniversity of TorontoMcMaster University
Fundersnot available
KeywordsMedicineSubspecialtyOrthopedic surgeryTraumatologyFamily medicinePhysical therapySurgery

Abstract

fetched live from OpenAlex

Background: Orthopaedic surgery suffers from gender disparity, and annual conferences are visible opportunities to quantify gender representation within a field. Therefore, the purpose of this manuscript was to investigate the prevalence of female speakers and moderators, and male-only panel sessions, at 10 major Orthopaedic Surgery meetings. Methods: Conference programs and details of faculty moderating or presenting in 10 Orthopaedic Surgery annual meetings in 2021 were retrieved. Conferences were selected with the aim of size and diversity in subspecialty topics and included American Association of Hip and Knee Surgeons, American Association for Hand Surgery, American Academy of Orthopaedic Surgeons, American Orthopaedic Society for Sports Medicine, Canadian Orthopaedic Association (COA), European Federation of National Associations of Orthopaedics and Traumatology, North American Spine Society, Orthopaedic Research Society (ORS), Orthopaedic Trauma Association, and Pediatric Orthopaedic Society of North America (POSNA). Primary outcomes included percentage of female chairs and speakers and percentage of male-only panels, while secondary outcomes included number of publications, number of citations, and H-indexes of faculty. Further subgroup comparisons were performed between male-only panels and non-male-only panels and female members and male members. Results: Of 207 included sessions, 121 (58.5%) were male-only panels and 150 (12.6%) of 1,188 faculty members were women. Conferences organized by the COA, ORS, and POSNA had higher percentages of female representation, while spine surgery and adult hip/knee reconstruction sessions had more than 70% male-only panels and fewer than 10% female members. There were no significant differences between male members and female members regarding years of practice; however, male members were more likely to hold the title of professor (p < 0.001). Male members and female members stratified by quartiles of publications, citations, and H-indexes, moderated or participated in similar numbers of sessions, indicating an absence of selection bias. Conclusions: There is a high prevalence of male-only panels (58.5%) and an overall lack of female representation (12.6%) in 10 major Orthopaedic Surgery meetings. Male members and female members from these conferences were found to have similar qualifications academically. Specific strategies such as the elimination of male-only panels, selecting diverse conference organizers, and forming conference equity, diversity, and inclusion committees can help achieve cultural change. Level of Evidence: Level V.

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)0.005
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.001

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 head0.483
GPT teacher head0.549
Teacher spread0.065 · 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 designObservational
DomainIncentives
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
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueJBJS Open AccessSame topicDiversity and Career in MedicineFrench-language works237,207