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<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<0.0001). Europe and the Americas had the most female academic medical contributors (p<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. 

1) Biological gender of authors  

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. 

2) Geographical demographics  

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.  

3) Presence of at least one female author

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.  

4) Author’s qualifications 

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).  

5) Source of funding  

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.