Gender equality in leadership of <scp>HIV</scp> care cascade clinical trials: A methodological study
Bibliographic record
Abstract
OBJECTIVES: Equitable representation in research leadership is essential across all areas of medical science. In the context of HIV-where women are disproportionately affected-examining gender distribution in the leadership of HIV trials is essential to assess progress towards equity and identify persisting barriers. METHODS: We conducted a methodological study of trials from the CASCADE database, which evaluates interventions to improve the HIV care cascade. We extracted first and last authors' names and used Genderize.io to determine their gender, classifying authors as 'women' if the probability was 60% or greater. The primary outcome was the proportion of trials with women in leadership (first or last author), with secondary outcomes examining the proportions of trials with women as: first authors, last authors and in both roles. We also assessed associations with country income level, focus on women participants, study setting, pragmatism and team size. RESULTS: Gender for both authorship roles could be determined in 332 trials, of which 233/332 (70.2%) had a woman first or last author; 169/334 (50.6%) had a woman first author; 143/337 (42.4%) had a woman last author and 74/332 (22.3%) featured women in both roles. Women's leadership increased over time but was not associated with country income level, gender focus, study setting or impact factor. Effectiveness trials and those with fewer authors were more likely to have women in leadership. CONCLUSIONS: Women's leadership in HIV trials has increased, reflecting progress in gender equity. However, smaller author teams appear to facilitate women's leadership, suggesting barriers in larger collaborations. Continued efforts are needed to ensure sustained progress and equitable representation.
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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 | 0.562 | 0.750 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".