Authorship characteristics of hematology and oncology education chapters: A comprehensive analysis.
Bibliographic record
Abstract
460 Background: Gender and geographic disparities in academic medicine, along with conflict of interest in medical education, necessitate examination for promoting equity and inclusivity in hematology and oncology education. This study presents the first analysis of authors' gender, authorship country diversity, and conflict of interest status in the American Society of Clinical Oncology (ASCO) and the American Society of Hematology (ASH) educational books. Methods: A cross-sectional analysis was conducted on articles from ASCO, encompassing all volumes from 2012 up to 2022 and ASH educational books including all volumes from 2000 up to 2022. Author information was obtained from the ASCO and ASH educational book websites. Results: The analysis encompassed 2,784 articles. Females accounted for 44.2% of first authors and 37.6% of last authors in ASCO educational books, as well as 38.8% of first authors and 38.5% of last authors in ASH educational books. Among educational articles, the lowest female participation was in articles discussing colorectal cancer (26.7%), followed by global health (28.0%) and tumor biology (31.0%). The number of females first and last authors in ASCO and ASH educational articles increased significantly over time (p < 0.001). The presence of international authors in articles remained relatively stable. Among articles with international female first authors, Canada had the highest proportion (24.4%), followed by the UK (18.4%) and Italy (11.0%). Similarly, for international female last authors, Canada ranked highest (26.2%), followed by the UK (20.5%) and Italy (12.0%). Repeated authorship roles along with diversity of authorship institutions will be further investigated. Of the articles analyzed, 59.6% reported at least one author with a conflict of interest, while 26.7% reported no conflict of interest for any author. Information on conflict of interest was not provided in 13.7% of the articles. Research funding was the most common type of conflict of interest (47.7%), followed by honoraria (33.4%) and travel accommodations and expenses (15.8%). The prevalence of conflicts of interest increased slightly over time, but not significantly. Conclusions: Our study suggests that female authorship has increased significantly over time. International authorship remained stable, with Canada having the highest proportion. To build on this positive momentum, it is essential to continue our efforts in further increasing female representation, promoting transparency in conflict-of-interest reporting, and mitigating potential biases in medical education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".