MétaCan
Menu
Back to cohort
Record W4403135233 · doi:10.1016/j.jsurg.2024.09.001

Exploring Gender Diversity in Canadian Surgical Residency Leadership

2024· article· en· W4403135233 on OpenAlexaffabout
Kaitlyn Harding, A.J. Lowik, Caroline Guinard, Sam M. Wiseman

Bibliographic record

VenueJournal of surgical education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsSt. Paul's HospitalDalhousie UniversityUniversity of British ColumbiaMemorial University of Newfoundland
Fundersnot available
KeywordsDiversity (politics)Medical educationMedicinePsychologyFamily medicinePolitical scienceLaw

Abstract

fetched live from OpenAlex

• Cis men continue to outnumber physicians of any other gender in surgical residency leadership. • Cis women hold leadership positions across surgical subspecialties and provinces. • General surgery residency programs have more cis women in leadership than expected. • Trans people are virtually absent from surgical residency leadership. • The lack of gender diversity in surgical leadership merits further study. Studies in the United States demonstrate a low proportion of cisgender women in medical leadership. No research exists about the prevalence of transgender people in medical leadership. The objective of this study was to evaluate gender representation within Canadian surgical training leadership. This study represents a survey based exploratory analysis and literature review. Associations between gender and leadership position, surgical subspecialty, years in practice and leadership role, province of work, and age were calculated using Chi squared goodness of fit and independence tests. The study was based out of the University of British Columbia in Vancouver and included all Canadian surgical training programs. Participants were identified using the Canadian Resident Matching Service and program websites. All prospective respondents (359) were emailed an encrypted survey link. The survey response rate was 65/359 responses (18%). The overall gender distribution was cis men (n = 36, 56.5%), cis women (n = 26, 40%), nonbinary (n = 1, 1.5%), agender (n = 1, 1.5%) and nonresponse (n = 1, 1.5%). Sixty-three percent of program directors were cis men, 33% were cis women and 4% were agender. Sixty-seven percent of associate program directors were cis women and 33% were cis men. Sixty-five percent of division leads were cis men, 29% were cis women, and 6% were nonbinary. There were more cis women in general surgery leadership than expected (df = 1, N = 20, x 2 = 11.05, p ≤ 0.001). No statistically significant associations between gender identity/modality, leadership role, province, or age were found using chi squared tests. Cis men continue to outnumber all others in surgical training leadership. More cis women than expected work in general surgery training leadership. However, these findings must be interpreted with caution considering the low survey response rate and the greater proportion of cis women respondents compared to cis women surgeons. There is a marked absence of binary-identified trans people in surgical training leadership in Canada, however a small number of nonbinary and agender people are present.

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)0.011
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.995
Threshold uncertainty score0.229

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0070.010
Science and technology studies0.0060.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.380
GPT teacher head0.369
Teacher spread0.011 · 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
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

Citations3
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueJournal of surgical educationSame topicDiversity and Career in MedicineFrench-language works237,207