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Record W2984039058 · doi:10.5489/cuaj.6117

Gender disparity within academic Canadian urology

2019· article· en· W2984039058 on OpenAlexaffvenueabout
Julius Vladimir Ilin, Émilie Langlois, Sabeena Jalal, Faisal Khosa

Bibliographic record

VenueCanadian Urological Association Journal · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsUniversity of British ColumbiaUniversity of Ottawa
Fundersnot available
KeywordsUrologyMedicinePsychology

Abstract

fetched live from OpenAlex

INTRODUCTION: Increasing female matriculation into medical school has shown an increase in women training in academic urology, but gender disparity still exists within this male-dominated field. This study aims to evaluate publication productivity and rank differences of Canadian female and male academic urologists. METHODS: was consulted to tabulate the number of documents published, citations, and h-index of each faculty member. To account for temporal bias associated with the h-index, the m-quotient was also computed. RESULTS: There was a significantly higher number of men (164, 88.17%) among academic faculty than women (22, 11.83%). As academic rank increased, the proportion of female urologists decreased. Overall, male urologists had higher academic ranks, h-index values, number of publications, and citations (p=0.038, p=0.0038, p=0.0011, and p=0.014, respectively). There was an insignificant difference between men and women with respect to their m-quotient medians (p=0.25). CONCLUSIONS: There is an increasing number of women completing residency in urology, although there are disproportionally fewer female urologists at senior academic positions. Significant differences were found in the h-index, publication count, and citation number between male and female urologists. When using the m-quotient to adjust for temporal bias, no significant differences were found between the gender in terms of academic output.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.561
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.027
GPT teacher head0.254
Teacher spread0.228 · 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; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
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

Citations27
Published2019
Admission routes3
Has abstractyes

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