Does Gender Predict Research Productivity? The Case of Prolific Suicidologists

<b></b> <i>Aims</i>: While there is substantial literature on gender and research productivity, bearing mixed results, no study is available for suicidology. The present investigation fills this gap and focuses on an influential elite. <i>Methods</i>: Data are taken from the Web of Science (WoS). They refer to the most prolific suicidologists (<i>N</i> = 116) with 70 or more works on the subject of suicide cited in WoS. Measures of research productivity include the number of works on suicide, citations to these works, and the h-index. The link between gender and the measures of research productivity is adjusted for years of experience, membership in a local research cluster, and region of the world. <i>Results</i>: Adjusting for the other predictors, males had more publications than females. However, gender did not predict either measure of quality of research (citations, h-index). Years of experience, as well as membership in a research cluster, predicted research productivity in most analyses. Region was unrelated to research productivity. <i>Limitations</i>: Further work on productivity might assess additional potential predictors including marital status, grant funding, and presence of young children. <i>Conclusion</i>: There is no significant difference between the genders in research quality. Similar results have been noted in previous work on prolific scientists.

Authors

Institutions

Publication Details

Journal
Crisis
Published
2024-10-09
DOI
https://doi.org/10.1027/0227-5910/a000973
Citations
2
Primary Topic
scientometrics and bibliometrics research
Type
article
Field-Weighted Citation Impact
0.92
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Does Gender Predict Research Productivity? The Case of Prolific Suicidologists

David Lester, Steven Stack
2 citations
Crisis
scientometrics and bibliometrics research
0.92
article

Does Gender Predict Research Productivity? The Case of Prolific Suicidologists

David Lester, Steven Stack
article en
2 citations

Abstract

<b></b> <i>Aims</i>: While there is substantial literature on gender and research productivity, bearing mixed results, no study is available for suicidology. The present investigation fills this gap and focuses on an influential elite. <i>Methods</i>: Data are taken from the Web of Science (WoS). They refer to the most prolific suicidologists (<i>N</i> = 116) with 70 or more works on the subject of suicide cited in WoS. Measures of research productivity include the number of works on suicide, citations to these works, and the h-index. The link between gender and the measures of research productivity is adjusted for years of experience, membership in a local research cluster, and region of the world. <i>Results</i>: Adjusting for the other predictors, males had more publications than females. However, gender did not predict either measure of quality of research (citations, h-index). Years of experience, as well as membership in a research cluster, predicted research productivity in most analyses. Region was unrelated to research productivity. <i>Limitations</i>: Further work on productivity might assess additional potential predictors including marital status, grant funding, and presence of young children. <i>Conclusion</i>: There is no significant difference between the genders in research quality. Similar results have been noted in previous work on prolific scientists.

CrisisVol. 45(6)
Wayne State University (US), Stockton University (US)
Gender equality
Openalex Percentile: Top 21%
scientometrics and bibliometrics research
0.92
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.