Knowledge Syntheses in Medical Education: Examining Authors’ Gender, Geographic Location, and Institutional Affiliation
Bibliographic record
Abstract
In medical education, researchers are encouraged to publish knowledge syntheses and educators to apply those findings. 1,2 Knowledge syntheses are produced by author teams, which are required to make many, often subjective decisions during the review process. These decisions can impact the conduct and conclusions of knowledge syntheses, creating important implications for the field’s evidence base. Additionally, author decisions can be guided by author characteristics and the institutional cultures and power structures within which they operate. In medical education, we know little about who writes knowledge syntheses and thus do not know which author voices dominate or are absent. The purpose of this study is to examine and describe the characteristics of knowledge syntheses authors, focusing on gender, geographical location, and institutional affiliation. This work is meant to illuminate who is creating the evidence base in medical education through their knowledge synthesis efforts. We conducted a case study of authors of knowledge syntheses published between 1999 and 2019 that included citations for 963 knowledge syntheses in 14 core medical education journals. We created a publicly accessible dataset in 2020 3 and enriched it by characterizing the authors using Genderize.io, a gender prediction tool, the World Bank Country Classification, and Times’ Higher Education World University Rankings 2020. We calculated descriptive statistics using Google Sheets, and we visualized the data using Tableau. We identified 4,110 authors across all authorship positions, of which 3,199 were unique authors. The number of authors per knowledge synthesis ranged from 1 to 60 with an average of 4.31 (SD = 3.07, median = 4). Seventy-nine knowledge syntheses (8.2%) were single-author publications. Over the 20-year time period analyzed, the average number of authors per knowledge synthesis increased (M = 1.80 in 1999; M = 5.34 in 2019). We identified the gender of 4,052 author names. Knowledge syntheses were authored by 2,047 females (50.5%) and 2,005 males (49.5%) across all author positions. More females were listed as first (n = 494; 51.9%) and second authors (n = 483; 55.4%), whereas last author (for those papers with more than 1 author) was held by more males (n = 404; 56.0%). Across all authorship positions, authors listed affiliations in 58 countries. Fifty-four knowledge syntheses (5.6%) included authors from low- or middle-income countries (LMIC). By number of knowledge syntheses, the United States (n = 366; 38%), Canada (n = 233; 24%), and the United Kingdom (n = 180; 19%) were most represented. The most countries represented on a single-author team were 7. Eighty percent (n = 767) of knowledge syntheses included authors from a single country only, who were predominantly located in the United States (n = 271; 22%), Canada (n = 149; 12%), and the United Kingdom (n = 122; 10%). First authors represented 374 unique institutions with the greatest representation from the University of Toronto (n = 55; 6%) and the Mayo Clinic (n = 32; 3%). Of the top 100 ranked institutions, only 58 were represented; yet this group accounted for 35% (n = 335) in our sample. Author characteristics, such as gender, geographical location, and institutional affiliation, can influence the nature of knowledge syntheses and inadvertently reinforce dominant power structures. The number of authors per knowledge syntheses has grown over the last 2 decades, while concurrently, the overall percentage of female authors across all authorship positions has grown. Author positions were dominated by North American authors, with a minority of authors located in LMIC. Authors from highly ranked institutions wrote a large proportion of the knowledge syntheses in our sample. To better understand the origins of medical education’s evidence base, we must first understand the characteristics of the authors who publish knowledge syntheses. The authors wish to thank Alison Whelan, MD, Chris Hanley, and Beatrice Schmider, all at the Association of American Medical Colleges (AAMC), and all of the members of the Core Entrustable Professional Activities for Entering Residency (Core EPAs) pilot for their support, inspiration, and contributions to the work that led to this report. All participating Core EPA pilot institutions and individuals can be found at https://www.aamc.org/initiatives/coreepas/pilotparticipants/.
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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 | 0.231 | 0.625 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.028 | 0.033 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".