Ethnicity and gender trends of UK authors in The British Medical Journal and the Lancet over the past two decades: a comprehensive longitudinal analysis
Bibliographic record
Abstract
Background While gender equity among academic authors has been extensively investigated, there is a significant gap in our understanding of racial/ethnic authorship trends, despite the recognition of barriers to authorship along both ethnic and gender lines. Leveraging the meta-data for all articles published in The British Medical Journal (The BMJ) and the Lancet and between 2002 and 2022 (inclusive), we explore demographic trends among UK academic medicine authors in two of the world's leading British medical journals. Methods We systematically searched PubMed's MEDLINE for all articles published in The BMJ and Lancet between January 1st 2002 and December 31st 2022. Filtering for articles with a UK affiliation, we predicted gender using a publicly-validated name-to-gender dictionary, while data was analysed to explore and investigate ethnicity using the Consumer Data Research Centre's (CDRC) Ethnicity Estimator. Data was analysed to explore and investigate: (a) the proportion of female/male author publications, (b) the proportion of the various UK author ethnicity groups, and (c) the overlap/intersection between gender and ethnic identities among first and last authors. This comprehensive longitudinal analysis was conducted on 82,143 articles (51,209 from The BMJ and 30,934 from the Lancet ) which represents >97% of all published articles between 2002 and 2022. As we sought to understand how academic authorship reflects the diversity of the UK population, we limited our analysis to first and last authors who had a UK affiliation and excluded "news" and "comments" pieces (16,736 articles for The BMJ and 4678 articles from the Lancet ). The main outcome measures were the trends in first and last authorship demographics of academic medicine, focusing on the proportion of female/male authors, ethnicity and their intersectionality. Findings Our findings show that, while women have made substantial headway towards equity among first and last authorship in The BMJ (peaking at 42% and 43%), they remain under-represented in the Lancet (35% and 27%). In both The BMJ and Lancet , Black authors have remained severely under-represented as both first and last authors (below 1% for most of the two decades), while Asian authors have increased proportionally to match their fraction in the general population (ranging from 2 to 10%). Interpretation Analysis over the past two decades has shown that the gender author gap is decreasing quickly in The BMJ and Lancet . However, despite the two journals' growing focus on structural inequalities in medical academia, little progress has been made in rectifying the large gap between White British authors and other ethnic groups, especially Black authors. Without more awareness, diversity initiatives which have resulted in positive gains for White women do not seem to translate well for authors of colour. Funding None.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".