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Record W3021276380 · doi:10.7759/cureus.7917

Prevalence of Gender Disparity in Professional Societies of Family Medicine: A Global Perspective

2020· article· en· W3021276380 on OpenAlexaffabout
Aven Sidhu, Sabeena Jalal, Faisal Khosa

Bibliographic record

VenueCureus · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsVancouver General HospitalFraser Health
Fundersnot available
KeywordsGender disparityMedicineObservational studyProductivityPerspective (graphical)Women PhysiciansGender equalityProfessional associationFamily medicineCross-sectional studyDemographyPublic relationsGender studiesEconomic growthPathologyPolitical science

Abstract

fetched live from OpenAlex

Introduction Gender disparity in academic and organizational settings has been the topic of numerous studies, which have attributed under representation of females within medicine to both individual and institutional reasons. The main objective of our study was to assess gender disparity in leadership positions in committees of professional societies of family medicine (FM). Methods In this cross-sectional observational study, we collected publicly available information from 3 major FM societies (College of Family Physicians of Canada, the Royal Australian College of General Practitioners, and the World Organization of Family Doctors) and also collected the academic/leadership information for each committee member, including bibliometric parameters of their research productivity. Results In total, our sample size was 960 and composed of 58% men (556) and 42% women (404). There was a significant difference found in all the research productivity variables. Men had a greater number of publications, number of citations, years since first publication, years of active research, and had a larger h-index. Conclusion In conclusion, gender disparity within FM societies is less significant compared to other professional medical societies and creating an environment that supports women and supports research can aid in achieving gender parity.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.380
Teacher spread0.280 · 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 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

Citations5
Published2020
Admission routes2
Has abstractyes

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