Authorship Diversity in General Surgery Related Cochrane Systematic Reviews
Bibliographic record
Abstract
Background This study sought to determine the gender and country diversity in authorship representation in the authorship of Cochrane systematic reviews related to General Surgery. Methods We searched and extracted data from the Cochrane Library on 3 September 2022 using ‘keyword:General surgery’, and included published reviews, protocols, and withdrawn publications. We extracted authors’ details and searched online to determine their gender, attempting to capture at least one webpage demonstrating it. Authors whose gender could not be ascertained were excluded from gender-based analyses. For graphical representation, we used a choropleth-style map. We treated a collaborative author group belonging to a single country, e.g., MRC Clinical Trials Unit (UK), as a single author. A second author independently cross-verified the extracted data. Result Two hundred and fifty publications with a total of 1420 authors were included in the current study. Four authors had affiliation to two countries. The leading five represented nations (Figure 1A) in authorship were United Kingdom (n=562, 39.4%), China (n=163, 11.5%), Italy (n=144, 10.1%), Canada (n=91, 6.4%), and United States of America (n=89, 6.2%). Syria is the only country among all the low-income countries which had authorship representation and constituted 0.34% (n=5) of all the authors. India (n=8, 0.6%) and Nigeria (n=2, 0.1%) were the only countries from lower-middle income groups who had representation. Male (n=957) to female (n=453) ratio in this study was 2.11:1 (Figure 1B). Sex data for ten authors couldn’t be retreived and were categorized as ‘unknown’ group. There were 169 (67.3%) male and 82 (32.6%) female first authors (sex ratio 2.06:1). One study had designated two authors as co-first authors. Women (n= 81) constituted 32.4% of all the corresponding authors (sex ratio 2.06:1). One article didn’t have any designated corresponding author. One hundred and fifty (60%) studies didn’t have any female representation in any lead author (corresponding or first author) position. Fifty-eight (23.2%) studies didn’t have any female authors at all, whereas in contrast there were only eight studies (3.2%) which did not have any male authors. Conclusion Authors from high-income countries continue to be the largest contributors to Cochrane systematic reviews in General Surgery, source of one of the highest quality evidence. There is extremely poor representation of female authors and authors from low and low-middle-income countries. Active capacity-building efforts are needed in several countries for advancing authorship diversity.
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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 | 0.341 | 0.760 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.048 | 0.058 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".