Exploring parity in female authorship of pharmacoepidemiology articles: A case study of the Canadian Network for Observational Drug Effect Studies and its citing articles
Bibliographic record
Abstract
Purpose The Canadian Network for Observational Drug Effect Studies (CNODES) studies the benefits and risks of post-market drugs and evaluates its research mobilization efforts for accountability, demonstrating value, and learning. As part of these evaluation efforts, and acknowledging gender disparity in authorship across many academic disciplines, CNODES examined the relationship between gender and authorship in its own journal articles and the literature citing them. Methods CNODES articles (published 2012–2017) and all citing articles were identified and extracted using Scopus. Scopus author IDs were used to extract full names and a web service (www.genderapi.com) was used to estimate gender, converting all probabilities <80% to “indeterminate.” T-tests and visualizations were used to compare the proportion of females between CNODES and the citing literature. Results Twenty-eight CNODES articles and 463 citing articles were identified. The mean number of authors per article was 9.5 in CNODES articles and 5.7 in the citing literature. CNODES articles had a female authorship rate of 36%, compared to 29% in the citing literature (7% difference, 95% CI: [1%, 13%]). There were no female authors in 14% of CNODES articles versus 36% of the citing literature. Women were first authors in 25% and corresponding authors in 14% of CNODES articles. Conclusions This analysis provides a benchmark and method to monitor progress in female parity in pharmacoepidemiology authorship. Further work is needed to determine and address barriers and facilitators to women's recruitment and advancement in the field of pharmacoepidemiology.
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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 | 0.028 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.028 | 0.043 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".