How global is global health research? A large-scale analysis of trends in authorship
Bibliographic record
Abstract
Many have called for greater inclusion of researchers from low- and middle-income countries (LMICs) in the conduct of global health research, yet the extent to which this occurs is unclear. Prior studies are journal-, subject-, or region-specific, largely rely on manual review, and yield varying estimates not amenable to broad evaluation of the literature. We conducted a large-scale investigation of the contribution of LMIC-affiliated researchers to published global health research and examined whether this contribution differed over time. We searched titles, abstracts, and keywords for the names of countries ever classified as low-, lower middle-, or upper middle-income by the World Bank, and limited our search to items published from 2000 to 2017 in health science-related journals. Publication metadata were obtained from Elsevier/Scopus and analysed in statistical software. We calculated proportions of publications with any, first, and last authors affiliated with any LMIC as well as the same LMIC(s) identified in the title/abstract/keywords, and stratified analyses by year, country, and countries' most common income status. We analysed 786 779 publications and found that 86.0% included at least one LMIC-affiliated author, while 77.2% and 71.2% had an LMIC-affiliated first or last author, respectively; however, analagous proportions were only 58.7%, 36.8%, and 29.1% among 100 687 publications about low-income countries. Proportions of publications with LMIC-affiliated authors increased over time, yet this observation was driven by high research activity and representation among upper middle-income countries. Between-country variation in representation was observed, even within income status categories. We invite comment regarding these findings, particularly from voices underrepresented in this field.
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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 | 0.093 | 0.380 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.035 | 0.096 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".