Gender disparities in coronavirus disease 2019 clinical trial leadership

Çevik, Müge; Haque, Syed Arefinul; Manne‐Goehler, Jennifer; Kuppalli, Krutika; Sax, Paul E.; Majumder, Maimuna S.; Orkin, Chloe

doi:10.1016/j.cmi.2020.12.025

Cited by 29 publications

(33 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using tools to infer gender from first name (or first name and surname) can be helpful in medical research, as it is often considered an effective way to save time and resources. For example, using genderize.io, Cevik et al compared the gender distribution of clinical trial leadership in COVID-19 [1]. They found that only 28% of principal investigators among COVID-19 studies were female, compared to 55% and 42% for breast cancer and diabetes trials over the same period, respectively.…”

Section: Introductionmentioning

confidence: 99%

Performance of gender detection tools: a comparative study of name-to-gender inference services

Sebo¹

2021

jmla

104

View full text Add to dashboard Cite

Objective: To evaluate the performance of gender detection tools that allow the uploading of files (e.g., Excel or CSV files) containing first names, are usable by researchers without advanced computer skills, and are at least partially free of charge.Methods: The study was conducted using four physician datasets (total number of physicians: 6,131; 50.3% female) from Switzerland, a multilingual country. Four gender detection tools met the inclusion criteria: three partially free (Gender API, NamSor, and genderize.io) and one completely free (Wiki-Gendersort). For each tool, we recorded the number of correct classifications (i.e., correct gender assigned to a name), misclassifications (i.e., wrong gender assigned to a name), and nonclassifications (i.e., no gender assigned). We computed three metrics: the proportion of misclassifications excluding nonclassifications (errorCodedWithoutNA), the proportion of nonclassifications (naCoded), and the proportion of misclassifications and nonclassifications (errorCoded).Results: The proportion of misclassifications was low for all four gender detection tools (errorCodedWithoutNA between 1.5 and 2.2%). By contrast, the proportion of unrecognized names (naCoded) varied: 0% for NamSor, 0.3% for Gender API, 4.5% for Wiki-Gendersort, and 16.4% for genderize.io. Using errorCoded, which penalizes both types of error equally, we obtained the following results: Gender API 1.8%, NamSor 2.0%, Wiki-Gendersort 6.6%, and genderize.io 17.7%.Conclusions: Gender API and NamSor were the most accurate tools. Genderize.io led to a high number of nonclassifications. Wiki-Gendersort may be a good compromise for researchers wishing to use a completely free tool. Other studies would be useful to evaluate the performance of these tools in other populations (e.g., Asian).

show abstract

Section: Introductionmentioning

confidence: 99%

Performance of gender detection tools: a comparative study of name-to-gender inference services

Sebo¹

2021

jmla

104

View full text Add to dashboard Cite

show abstract

“…Gender detection tools are increasingly used in medical research, particularly to explore the gender gap in scientific publications, grants allocations, salaries, or career advancement processes [1][2][3]. Their main advantage lies in the possibility of uploading large CSV or Excel files.…”

Section: Introductionmentioning

confidence: 99%

Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference

Sebo¹

2021

jmla

View full text Add to dashboard Cite

Objective: We recently showed that genderize.io is not a sufficiently powerful gender detection tool due to a large number of nonclassifications. In the present study, we aimed to assess whether the accuracy of inference by genderize.io can be improved by manipulating the first names in the database.Methods: We used a database containing the first names, surnames, and gender of 6,131 physicians practicing in a multicultural country (Switzerland). We uploaded the original CSV file (file #1), the file obtained after removing all diacritic marks, such as accents and cedilla (file #2), and the file obtained after removing all diacritic marks and retaining only the first term of the compound first names (file #3). For each file, we computed three performance metrics: proportion of misclassifications (errorCodedWithoutNA), proportion of nonclassifications (naCoded), and proportion of misclassifications and nonclassifications (errorCoded).Results: naCoded, which was high for file #1 (16.4%), was reduced after data manipulation (file #2: 11.7%, file #3: 0.4%). As the increase in the number of misclassifications was small, the overall performance of genderize.io (i.e., errorCoded) improved, especially for file #3 (file #1: 17.7%, file #2: 13.0%, and file #3: 2.3%).Conclusions: A relatively simple manipulation of the data improved the accuracy of gender inference by genderize.io. We recommend using genderize.io only with files that were modified in this way.

show abstract

“…These disparities have increased during the COVID-19 pandemic [1]. Reasons for this gender gap may include structural shortcomings in the academic setting such as unequal opportunities for women to progress in their careers, and unequal access to funding, grants and leadership positions [1,48]. Especially during early career stages, pregnancy and lack of child care may translate into prolonged career J o u r n a l P r e -p r o o f disadvantages, such as time constraints to publish and conduct research or difficulties to attend scientific conferences [49].…”

Section: Inequalities and Discrimination Based On Gender Economic Status And Ethnicity/racementioning

confidence: 99%

“…Women are also under-represented as researchers and senior clinicians, especially in ID [ 47 ]. These disparities have increased during the COVID-19 pandemic [ 1 ]. Reasons for this gender gap may include structural shortcomings in the academic setting, such as unequal opportunities for women to progress in their careers, and unequal access to funding, grants and leadership positions [ 1 , 48 ].…”

Section: Inequalities and Discrimination Based On Gender Economic Status And Ethnicity/racementioning

confidence: 99%

“…Furthermore, antimicrobial stewardship was largely neglected during the pandemic, resulting in a renewed need J o u r n a l P r e -p r o o f to reactivate this knowledge in CM and ID training. Pre-pandemic imbalances in women's access to and representation in research activities and funding aggravated during the COVID-19 pandemic [1]. Lastly, disproportionately high mortality and morbidity rates due to COVID-19 were incurred in socioeconomically disadvantaged populations and ethnic minorities.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Future developments in training

Power

Dellière

Velikov

et al. 2021

Clinical Microbiology and Infection

View full text Add to dashboard Cite

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

show abstract

Gender disparities in coronavirus disease 2019 clinical trial leadership

Cited by 29 publications

References 14 publications

Performance of gender detection tools: a comparative study of name-to-gender inference services

Performance of gender detection tools: a comparative study of name-to-gender inference services

Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference

Future developments in training

Contact Info

Product

Resources

About