Women doctors in female urology: current status and implications for future workforce
Bibliographic record
Abstract
OBJECTIVE: To objectively determine the percentage of female trainees and consultants who are interested in their career being focussed on female urology (FU) in order to facilitate the improved planning for the future of this sub-specialty. SUBJECTS AND METHODS: . The survey was sent to urology consultants and trainees who were female from Australia, New Zealand, and Canada. RESULTS: The total response rate to the survey was 61%. Up to 50% of female consultants and trainees selected a career in FU due to their gender, but up to 75% of respondents were also interested in FU of their own accord. Common concerns held by a majority of respondents included both the medical community's and the public's lack of awareness of FU as a component of urological expertise. Despite these concerns, most of the trainees were not concerned regarding their future work opportunities in FU, and many had intentions to pursue a fellowship in FU. CONCLUSION: Female urology is an increasingly popular sub-specialisation of urology, given the steady increase in the intake of female trainees. Similar trends were identified internationally. Urology training in this area will need to continue to increase the community's and the primary health care referrer's awareness in order to ensure the continued success and growth of the sub-specialty.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
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".