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Record W4402552095 · doi:10.1016/j.cjcpc.2024.09.002

Gender Distribution in Paediatric Cardiology Training Programs in Canada

2024· article· en· W4402552095 on OpenAlexaffabout
Michael Gritti, Megan Werger, Alison J Howell, Conall T. Morgan

Bibliographic record

VenueCJC Pediatric and Congenital Heart Disease · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsDistribution (mathematics)Training (meteorology)MedicineMedical educationInternal medicineCardiologyGeographyMathematics

Abstract

fetched live from OpenAlex

Background: Despite female medical students being the majority, there are certain medical specialities that continue to have a prevalence of male trainees, such as adult cardiology. The purpose of this study is to examine gender distribution within Canadian paediatric cardiology training programs. Methods: Both application and successful matches to core paediatric residency programs and paediatric cardiology programs were obtained through the Canadian Resident Matching Service. Analysis was performed to determine if the association between gender of paediatric residents and paediatric cardiology applicants/fellows was significant. Results: < 0.23). Conclusions: There is a gender discrepancy within Canadian paediatric cardiology training programs with a predominance of male trainees in the recent era. It appears that female under-representation in the field is due to the low number of female applicants. Although limited by sample size, there was no clear association between applicant gender and success of admission to a paediatric cardiology training program.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.392

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.038
GPT teacher head0.257
Teacher spread0.219 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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