DOI: 10.5061/dryad.rjdfn2zdh
title: Trends in authorship demographics for manuscripts published in Endocrine journals: A 70-year analysis

description: Background  Over the previous few decades, demographics, gender,
 and the amount of papers published have all changed considerably. One of
 the fields of medicine that has yet to be extensively investigated is
 endocrinology.  Material and Methods  Journal of
 Endocrinology and General & Comparative Endocrinology are two
 landmark journals that publish articles from around the world. We examined
 each decade during the 70-year period from 1961 to 2021. Funding source,
 first author – last author gender, their demographics and proportion of
 papers with at least one female author were the parameters considered
 while studying each publication. We predicted that the number of female
 authors per paper would increase with time, as would the range of degrees
 held by the authors, demographical variations in authorship, and the
 funding source. Our goal was also to determine the distribution of female
 first authors and senior authors in endocrinology journals over a 70-year
 period, as well as to check the gender combinations using the Punnett
 square.   Results  Female initial authors rose
 from 7% to 29.6% (p&lt;0.0006) between 1961 and 2021, whereas female
 senior authors rose from 15.6% to 22.2%. Despite women's small
 contributions to first and senior authors, female participation rose from
 17.48% (25/143) to 70% (170/250) between 1961 and 2021.
 Male-Female and Female-Male combinations rose with
 Chi-Square = 124.6, (p&lt;0.0001). Europe and the Americas had the
 most female academic medical contributors (p&lt;0.0001) Regardless of
 author status, female participation rose from 17.48% in 1961 to 68% in
 2021.  Conclusion   In papers published in
 endocrinology journals, there was a rising trend in female contributions
 to academic medicine. Even with the large growth of female
 endocrinologists, there is still a disparity in why the increase in female
 authors is comparably fewer. 
description: <em>1) Biological gender
 of authors </em>  The following approaches were used to collect
 data, in order of preference based on availability: To gather information
 from University/Organization/LinkedIn pages, use Google to search for
 writers by their names and departments. Assuming gender based on
 nomenclature conventions (e.g., John being the name of a male author;
 Christy being a female author).   The biological genders of some authors who were
 registered on Scopus and Google Scholar were used to assign genders. In
 addition, in the event of a snag, we used https://gender-api.com to
 resolve the issue.  <em>2) Geographical
 demographics  </em> The following approaches were used to collect
 data, in order of preference based on availability: Data from
 University/Organization/LinkedIn/ORCiD pages; Author affiliations
 specified in the article; data from University/Organization/LinkedIn/ORCiD
 pages Based on regional nomenclature conventions and trends (e.g., Asian
 names being Raja Gopalchandra, Li-Hu Wang, BK Gupta, Chandragouda Patil,
 etc.). Author affiliation was one of the variables used to assign
 demographics to the authors, therefore we put a lot of focus on where they
 were affiliated.   <em>3) Presence of at least one female
 author</em> To include all other authors engaged in the
 study, the presence of at least one female author was determined using the
 same methods used to determine the biological gender of the first and last
 authors.   <em>4) Author’s
 qualifications</em>  Because all qualifications overlap, authors were
 divided into broad categories. MBBS/MD/DM/DO was included in Category 1.
 Ph.D./MSc/MPH were placed in category 2, and other paramedical areas were
 placed in category 3. To prevent prejudice while reporting, we divided
 authors into groups that classified qualifications according to
 international criteria.   We also used UNESCO's international
 standard classification of education
 (http://uis.unesco.org/en/topic/international-standard-classification-education-isced).   <em>5) Source of funding  </em> Data on funding was gathered by skimming through articles and looking for keywords such as 'grant, "support,' 'fund,' 'acknowledgement,' 'thank,' and so on.18 We divided the articles into major groups such as government, university or organization, industry, self, other, and not stated. The majority of the available data came from the government, university, organization, or industry, with the exception of a few articles where funding was a problem, in which case we used the category ‘others’, and articles where funding was not available, in which case we used the category ‘not mentioned’.  In research with several funding sources, government-based sources were given priority over other sources.  Companies/Industry/commercial sources were given preference over organizational or university financing in other multi-source sponsored studies without any government backing.