Female representation in oncology leadership in Africa: Analysis of trends in the African organization for cancer research and training in cancer (AORTIC).
Bibliographic record
Abstract
11052 Background: Board membership in oncology organizations and invitation to speak at major oncology conferences is an established indicator of gender disparities in oncology leadership. Recent studies note an upward trend in female board membership and female speakers at oncology conferences in European and American contexts. However, little is known about trends of female representation in board membership and at oncology conferences in Africa. AORTIC is the premier African cancer organization and hosts a biennial conference, during which its council members are elected for a two-year term. The conference hosts over 1000 participants and is highly regarded internationally. As the largest gathering of African oncologists, the conference presents a unique opportunity for insight into workforce disparities and trends. This study presents an analysis of female council membership and speaker participation at the 2015, 2017, 2019 and 2021 AORTIC conferences. Methods: AORTIC conference programmes and AORTIC elections for council membership in 2015, 2017, 2019 and 2021 were analyzed for gender representation. Speakers were divided into categories including keynote speakers, facilitators, session speakers, panelists, and oral abstract presenters. For each AORTIC session, data was collected on speaker name, gender, and affiliated country. Participants who presented multiple times were counted multiple times, as their role often varied between sessions. Personal data and country affiliation was determined through online biographies of speakers and/or visual confirmation of professional photographs. A simple descriptive analysis of the data and trends is presented here. Results: Since 2015, the proportion of female speakers and council members at AORTIC has increased. In 2015, females accounted for 44% of speakers, in contrast to 55% in 2021. In 2021, females made up over 50% of all speaker categories except keynote. The proportion of female council members also increased, from 42% in 2015 to 69% in 2021. Trends in geographic representation of speakers were also analyzed for gender, revealing an increase in female speakers residing in Africa. (Table) Conclusions: The proportion of female speakers and council members at Africa’s largest cancer conference has increased significantly since 2015. Female speaker participation was higher than in North America and Europe in 2017 and surpassed 50 % in 2021. In addition, female council membership in AORTIC has surpassed 50%, up to 69% in 2021. These trends suggest a rise in female leadership in cancer research and training on the continent, indicating that strengthening local platforms in LMIC may be associated with improved gender assimilation. [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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".