Geography, gender, and collaboration trends among global health authors
Bibliographic record
Abstract
Background Imbalances in global health authorship have previously been documented, but the extent of the problem has yet to be examined longitudinally across many journals. This paper investigates the gender (2002-2020) and geographic distribution (2014-2020) of authors publishing in peer-reviewed global health journals. We also examined the amount of global health research collaboration among different income groups and continents. Methods This cohort study analyzes articles published in 46 peer-reviewed global health journals. Gender-API assigned genders to 190,809 individuals who authored a combined 33,854 articles. The country affiliations of authors were categorized by continent and World Bank income groups. Descriptive analyses were conducted to assess collaboration between first and last authors belonging to different World Bank income groups and continents. Findings Women made up 39.3% of global health authors, and there was a statistically significant increase in the proportion of women authors between 2002 and 2020. The proportion of all global health authors who are women was highest in high income countries (45.9%) and lowest in low income countries (28.2%). Authors from middle income countries comprised of an increasing proportion of global health authors between 2014 and 2020. For articles with multiple authors, 16.0% and 24.1% have first and last authors from different income groups and continents, respectively. Conclusions While women and LMIC researchers are increasingly represented in global health publications, authorship gaps continue to persist. More research on structural determinants is necessary to elucidate how we improve authorship equity and support underrepresented global health expertise.
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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 | 0.006 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".