Gender differences in Australian general practice trainees performing procedures related to women’s reproductive health: A cross-sectional analysis
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".