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Record W2918439302 · doi:10.31128/ajgp-03-18-4527

Gender differences in Australian general practice trainees performing procedures related to women’s reproductive health: A cross-sectional analysis

2018· article· en· W2918439302 on OpenAlexaff
Fariba Aghajafari, Amanda Tapley, Mieke van Driel, Andrew Davey, Simon Morgan, Elizabeth Holliday, Jean Ball, Nigel Catzikiris, Katie Mulquiney, Neil Spike, Parker Magin

Bibliographic record

VenueAustralian Journal of General Practice · 2018
Typearticle
Languageen
FieldMedicine
TopicSex and Gender in Healthcare
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineCross-sectional studyRuralityReproductive healthConfidence intervalOdds ratioFamily medicineGeneral practiceGynecologyDemographyPopulationEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Gender differences may exist in the performance of women’s reproductive procedures. The aim of this study was to investigate the prevalence and association of general practice registrars’ performance of women’s procedures with trainees’ gender, rurality of practice and in-consultation seeking of information or assistance. METHOD: This was a cross-sectional analysis of a cohort study of registrars’ consultations in 2010–17. Registrars recorded 60 consecutive consultations during each six-month training term. The outcome was performance of a procedure related to women’s reproductve health. RESULTS: Of 24,333 procedures performed in 332,700 encounters, 15,634 were on female patients and 6025 of those included procedures relating to women’s reproductive health; 5002 were Pap smears (20.6%). Only 235 (4.7%) Pap smears were performed by male trainees. Performing women’s procedures was significantly associated with trainees’ gender, with an adjusted odds ratio of 4.80 (95% confidence interval: 4.10, 5.61). DISCUSSION: Our findings suggest that a gender difference exists in general practice trainees’ frequency of performing women’s procedures. Male trainees require more opportunities and support from their preceptors, clinical settings and training programs to perform these procedures.

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)0.005
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.116
GPT teacher head0.439
Teacher spread0.323 · 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.

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

Citations4
Published2018
Admission routes1
Has abstractyes

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