Influence of sex, age and length of service on scientific productivity of medical research institution staff

Aim. To assess the influence of sex, age and length of service on scientific productivity (h-index in the RSCI, Scopus and Web of Science) of researchers of the National Medical Research Center for Therapy and Preventive Medicine.

Material and methods. The study sample was formed from the staff of the National Medical Research Center for Therapy and Preventive Medicine and consisted of scientists of various ranks. The study included 147 people (women, 103; men, 44); Information was collected on their age, education, length of service, academic degree, the position held and author-level metrics  — the h-index, obtained from three abstract and citation databases  — RSCI, Scopus and Web of Science. All study participants were divided into groups by sex and age categories — young (≤39 years), middle-aged (men, 40-60 years; women, 40-55 years) and older (men >60 years old; women >55 years).

Results. Analysis showed that 70,1% of the research team consists of women; 60%  — young and middle-aged scientists, and 40%  — older people. Among male researchers, compared with women, there are more doctors of science and high h-index values in the RSCI, Scopus and Web of Science. H-index value in the RSCI among male researchers is 47,3% due to the age and work duration at the National Medical Research Center for Therapy and Preventive Medicine, and among female researchers, 42,8% due to academic degree and length of service. The h-index in Scopus in men is influenced by age, in women  — by the presence of an academic degree, which explain, respectively, 19,7 and 18,1% of its variability. H-index value in the Web of Science in men is associated with the work duration, while in women, with an academic degree and length of service, which explain, respectively, 24,4 and 21,1% of its variance.

Conclusion. Women and young and middle-aged scientists prevail in research team pattern of the National Medical Research Center for Therapy and Preventive Medicine. However, male researchers make a more significant contribution to scientific productivity, assessed by the h-index in the RSCI, Scopus and Web of Science. Age and length of service have the greatest influence on scientific productivity for male researchers, while for women — presence of academic degree and length of service. It is necessary to continue research on the study of individual, motivational and institutional factors affecting the scientific productivity.

1. Hirsch JE. An index to quantify an individual’s scientific research output. Proc Natl Acad Sci U S A. 2005;102(46):16569-72. doi:10.1073/pnas.0507655102.

2. Akoev MA, Markusova VA, Moskaleva OV, Pislyakov VV. Нandbook on scientometrics: science and technology development indicators. 2st ed. Yekaterinburg, IPC UrFU. 2021; p. 211. (In Russ.) ISBN: 978-5-7996- 3154-3.

3. Order of the Ministry of Education and Science of the Russian Federation dated 05.03.14 N 161 with amendments and additions dated 17.07.15 and 29.11.17 “On the approval of the standard regulation on the commission for assessing the performance of scientific organizations performing research, development and technological work for civil purposes, and a standard methodology for assessing the effectiveness of scientific organizations performing research, development and technological work for civilian purposes”. (In Russ.) https://base.garant.ru/70682234.

4. Drapkina OM, Poddubskaya EA, Andresyuk EA, Kontsevaya AV. Factors associated with the effectiveness of scientific activities and the clarification of the departments’ science intensity dependences on the number of researchers in medical institutions. The Russian Journal of Preventive Medicine.2021;24(3):118-26. (In Russ.) doi:10.17116/profmed202124031118.

5. WMA Declaration of Helsinki — Ethical Principles for Medical Research Involving Human Subjects. 2013. Link is available on 25.09.20. https://www.wma.net/policy/current-policies/page/5/.

6. Association of International Pharmaceutical Manufacturers, Interrepublican Confederation of Consumer Societies, Russian Academy of Medical Sciences. National Standard of the Russian Federation GOST R 52379-2005 “Good clinical practice”. M.: Standartynform. 2006. (In Russ.) Ссылка активна на 22.06.21. http://www.gostrf.com/normadata/1/4293852/4293852873.pdf.

7. Poddubskaya EA, Gasanova LG, Uchevatkina NV, Drapkina OM. Certificate of state registration of the Database RUS № 2019620909 dated 29.05.21. (In Russ.)

8. Tomczak M, Tomczak E, Kleka P, Lew R. Using power analysis to estimate appropriate sample size. Trends in Sport Sciences. 2014;1(21):195-206. http://www.tss.awf.poznan.pl/files/2_Trends_Vol_21_no_4_2014_195.pdf.

9. Peter_Statistics. Crash Course. Analysing a nominal and ordinal variable. Part 3c: Effect size. [Electronic resource]. Accessed Juny 22, 2021. URL: https://peterstatistics.com/CrashCourse/index.html.

10. Rea LM & Parker RA. Designing and conducting survey research: a comprehensive guide. San Francisco: Jossey-Bass Publishers. 2014:352. ISBN: 978-1-118-76703-0.

11. Coolican H. Research methods and statistics in psychology (5th Edition). Routledge. 2009:350-96. ISBN: 13: 978-0-340-98344-7.

12. Gingras Y, Larivière V, Macaluso B, Robitaille JP. The effects of aging on researchers’ publication and citation patterns. PLoS One. 2008;3(12):e4048. doi:10.1371/journal.pone.0004048.

13. Subramanian R, Nammalvar N. Age, Gender and Research Productivity: A Study of Speech and Hearing Faculty in India. J Scientometric Res. 2017;6(1):6-14. doi:10.5530/jscires.6.1.2.

14. Costas R, Leeuwen TN, Bordons M. A bibliometric classificatory approach for the study and assessment of research performance at the individual level: The effects of age on productivity and impact. J Am Society for Information Science and Technology. 2010;61(8):1564-81. doi:10.1002/asi.21348.

15. Falagas ME, Ierodiakonou V, Alexiou VG. At what age do biomedical scientists do their best work? FASEB J. 2008;22(12):4067-70. doi:10.1096/fj.08-117606.

16. Prpić K. Gender and productivity differentials in science. Scientometrics. 2002;55:27-58. doi:10.1023/A:1016046819457.

17. Jacob J, Lamari M. Factors influencing research productivity in higher education: An empirical investigation. ForesightRussia. 2012;6(3):40-9. (In Russ.) doi:10.17323/1995-459X.2012.3.40.49.

18. Svider PF, Shah P, Folbe AJ, Eloy JA. Chapter 21. Gender Differences in Medical Research Productivity. In: Shoja MM, Arynchyna A, Loukas M, D’Antoni AV, Buerger SM, Karl M, Tubbs RS, eds. A Guide to the Scientific Career: Virtues, Communication, Research, and Academic Writing. 1st ed. JohnWiley & Sons, Inc; 2020;183-92. doi:10.1002/9781118907283.ch21.

19. Salthouse TA. Continuity of cognitive change across adulthood. Psychon Bull Rev. 2016;23:932-9. doi:10.3758/s13423-015-0910-8.

20. Lone FA, Hussain M. Gender Variations in Research Productivity: Insights from Scholarly Research. Library Philosophy and Practice (e-journal). 2017;1608. https://digitalcommons.unl.edu/libphilprac/1608.

21. Bentley P. Gender differences and factors affecting publication productivity among Australian university academics. Journal of Sociology. 2012;48(1):85-103. doi:10.1177/1440783311411958.

22. Gordon NJ, Nucci LP, West CK, et al. Productivity and Citations of Educational Research: Using Educational Psychology as the Data Base. Educational Researcher. 1984;13(7):14-20. doi:10.3102/0013189X013007014.