Bibliographic record
Abstract
Background: The importance of gender diversity is well recognised.Despite promising change in addressing gender disparity, there remains a significant gap in women's representation in urology.Worldwide, a lack of diversity has been observed at scientific meetings.This study analyses the trend in women's representation at the Urological Society of Australia and New Zealand Annual Scientific Meeting (USANZ ASM) over the last 9 years.Method: We retrospectively collected data from eight conference programs between 2014 and 2022, evaluating the trend in the proportion of women represented in various sections.A difference of proportions test was performed to determine whether a significant change was seen from year to year.Results: A significant increase in women's representation was observed, the most significant increase was evident in the last 3 years, chairpersons increased from 5.9% to 44.1% (P = 0.003), international speakers from 11.7% to 39.1% (P = <0.001),expert speakers from 19.1% to 42.0% (P = 0.002), and total speakers from 19.5% to 34.7% (P = <0.001).Conclusion: Our study shows that a significant increase in the representation of women at the USANZ ASM has been achieved over the last 3 years.Unfortunately, this increase in representation has not been mirrored in the number of women trainees and training applicants.Additionally, this increase in representation well exceeds the proportion of women who are USANZ members, and is unevenly distributed across topics.A positive change has been observed, the challenge remains in dealing with unconscious bias and balancing the fine line between inclusivity and tokenism.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.820 | 0.608 |
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".