Local and foreign authorship of maternal health interventional research in low- and middle-income countries: systematic mapping of publications 2000–2012
Bibliographic record
Abstract
Abstract Background Researchers in low- and middle-income countries (LMICs) are under-represented in scientific literature. Mapping of authorship of articles can provide an assessment of data ownership and research capacity in LMICs over time and identify variations between different settings. Methods Systematic mapping of maternal health interventional research in LMICs from 2000 to 2012, comparing country of study and of affiliation of first authors. Studies on health systems or promotion; community-based activities; and haemorrhage, hypertension, HIV/STIs and malaria were included. Following review of 35,078 titles and abstracts, 2292 full-text publications were included. Data ownership was measured by the proportion of articles with an LMIC lead author (author affiliated with an LMIC institution). Results The total number of papers led by an LMIC author rose from 45.0/year in 2000–2003 to 98.0/year in 2004–2007, but increased only slightly thereafter to 113.1/year in 2008–2012. In the same periods, the proportion of papers led by a local author was 58.4 %, 60.8 % and 60.1 %, respectively. Data ownership varies markedly between countries. A quarter of countries led more than 75 % of their research; while in 10 countries, under 25 % of publications had a local first author. Researchers at LMIC institutions led 56.6 % (1297) of all papers, but only 26.8 % of systematic reviews (65/243), 29.9 % of modelling studies (44/147), and 33.2 % of articles in journals with an Impact Factor ≥5 (61/184). Sub-Saharan Africa authors led 54.2 % (538/993) of studies in the region, while 73.4 % did in Latin America and the Caribbean (223/304). Authors affiliated with United States (561) and United Kingdom (207) institutions together account for a third of publications. Around two thirds of USAID and European Union funded studies had high-income country leads, twice as many as that of Wellcome Trust and Rockefeller Foundation. Conclusions There are marked gaps in data ownership and these have not diminished over time. Increased locally-led publications, however, does suggest a growing capacity in LMIC institutions to analyse and articulate research findings. Differences in author attribution between funders might signal important variations in funders’ expectations of authorship and discrepancies in how funders understand collaboration. More stringent authorship oversight and reconsideration of authorship guidelines could facilitate growth in LMIC leadership. Left unaddressed, deficiencies in research ownership will continue to hinder alignment between the research undertaken and knowledge needs of LMICs.
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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 | 0.033 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.082 | 0.070 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".