Longitudinal trends of "manels" and gender representation at the ASCO Genitourinary Cancers Symposium.
Bibliographic record
Abstract
2 Background: Gender disparity in academic medicine has been a longstanding issue. Efforts have been made to recognize this imbalance and increase inclusivity. Despite this, a recent study examining the prevalence of all-male panels (“manels”) found that female faculty are significantly underrepresented at urology meetings, and nearly two-thirds of the sessions were manels. Therefore, we aimed to investigate the prevalence and longitudinal trends of manels and gender representation across genitourinary oncology disciplines at the ASCO Genitourinary Cancers Symposium (GU ASCO). Methods: GU ASCO online programs from 2018-2021 were used to obtain faculty information. Data collected included perceived gender, medical specialty, and panel role (chair/moderator vs. non-chair/non-moderator). For year 2021, additional data about the panelists, including the number of publications, H-index, citations, and academic rank, was collected. The primary outcomes were the percentage of manels and proportion of female panelists over time. Additionally, female representation among chair/moderators and specialties were evaluated. Results: Among 83 sessions involving 317 faculty members, 227 (71.6%) were males (p<0.001), and 28 panel sessions (33.7%) were manels. Between 2018 and 2020, there was a decrease in the prevalence of manels from 45% to 21.7%, but in 2021, it rose to 32.0%. The proportion of female panel members increased over time from 17.1% in 2018 to 35.7% in 2021 (p=0.012). The role of chair/moderator was predominantly represented by males (67.2%, p<0.001). The proportion of male panelists was particularly high in urology (91.2%, p<0.001) and radiation oncology (81.8%, p=0.002) compared to medical oncology (54.6%). In 2021, male speakers held higher academic rank (i.e. professor, associate, assistant) (p=0.020) and had a greater number of publications (p=0.003), H-index (p=0.009), citations (p=0.014) than females (Table). Conclusions: Over time, the number of female panelists increased with a corresponding decrease in proportion of manels, with the exception of 2021. Future studies that include data on meeting participant demographics will provide insight on whether panelists are over/under-represented in proportion to the audience. While improvements in male and female representation have been made over the years, meeting organizers should strive for representation that reflects a diversity of expertise and perspectives. [Table: see text]
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".