Pandemic and Gender Influences on Submissions to Epidemiology
Bibliographic record
Abstract
The COVID-19 pandemic substantially upended employment and career productivity,1 and these burdens have been disproportionately borne by women.2 Shortly after the onset of the pandemic, there were already signs that women’s research careers were disproportionately affected.3 The US National Academies of Sciences, Engineering, and Medicine Committee on Women in Science, Engineering, and Medicine has called for documentation of how the COVID-19 pandemic disrupted women’s research productivity.4 Among academics and their peers in similar government and nongovernmental organizations, publication productivity is an important metric of success used in hiring and promotion decisions. We evaluated the extent to which there were measurable and sustained differences in submissions to Epidemiology after the onset of the COVID-19 pandemic and, if so, how these differences varied by the imputed gender of the authors. Original Articles, Brief Reports, and Validation Studies submitted to Epidemiology from 1 January 2018 through 30 April 2022 were included to bracket the timeframe surrounding the declaration of the SARS-CoV-2 global pandemic. We recognize that the timing of pandemic disruptions emerged at different times around the world. For the purposes of this analysis, we chose 11 March 2020 as the start time of the pandemic, coinciding with the date of the official WHO declaration. We categorized submissions preceding this date as prepandemic declaration and submissions from 11 March 2020 to 30 April 2022 as postpandemic declaration. For each of the 3008 submissions encompassing 2308 unique first, last, or corresponding authors, we extracted the full name and order placement of each author, corresponding author status, and date of first submission from the submission management database. Authors of single-author papers were assigned to all three categories. To assign the probable gender of each author, we first used Genderize.io (Demografix Aps; Roskilde, Denmark) to impute a gender (masculine or feminine) for their first name. Genderize.io assigns genders to names along with proportions, ranging from 0.50 to 1, corresponding to the gender proportion of persons who have that name in the Genderize.io database of more than 114 million names with associated genders collected from around the world. We labeled names for which a gender could not be imputed as “unknown” (Table 1 and eFigure; https://links.lww.com/EDE/B998). We restricted the imputed genders assigned by Genderize.io to those with a proportion of 0.85 or greater. For authors whose genders were deemed “unknown” or had a Genderize.io proportion less than 0.85, we attempted to assign the gender by other means. Many of these authors were included in a previous self-study conducted by Epidemiology.5 For these authors, we accepted the genders assigned to them by Kiang et al via their internet research; we conducted similar searches, with a focus on pronouns or picture identification to infer the gender of these authors by virtue of masculine or feminine presenting, for the remaining authors. For this analysis, we categorized authors’ imputed gender based on being either a masculine name or feminine name. We acknowledge that the imputed genders of the authors’ names may not be consistent with either the sex or gender identity of the author and that the binary classification of gender does not encompass all gender identities. The distribution of authors by imputed gender and probability is shown in Table 1. For first, last, and corresponding authors, we analyzed the average number of submissions to the journal per week, pre- and postpandemic declaration. TABLE 1. - Imputed Genders of the Authors Submitting Articles to Epidemiology 1 January 2018 through 30 April 2022 by Author Group Author Name Gender Imputation (n) (%) First author Unknown 227 7.5 Imputed 2,781 92 Genderize.io 2,271 76 Web research 510 17 Feminine name 1,307 47 Masculine name 1,474 53 Last author Unknown 121 4.0 Imputed 2,887 96 Genderize.io 2,616 87 Web research 271 9.0 Feminine name 1,041 36 Masculine name 1,846 64 Corresponding author Unknown 180 6.0 Imputed 2,828 94 Genderize.io 2,318 77 Web research 510 17 Feminine name 1,187 42 Masculine name 1,641 58 Total 3,008 100 During this period, the average weekly submissions to Epidemiology varied by imputed gender and showed changes in average submissions following the declaration of the pandemic (Table 2). For first authors, average weekly submissions from authors with feminine names showed a decrease of −0.66 per week (95% confidence interval [CI]: −0.31, −1.00) and submissions from authors with masculine names showed an increase of 0.47 per week (95% CI: 0.09, 0.85) following the pandemic declaration. Finally, for authors with corresponding status, average weekly submissions from authors with feminine names showed a decrease of −0.40 per week (95% CI: −0.07, −0.73) and submissions from authors with masculine names showed an increase of 0.85 (95% CI: 0.46, 1.24). TABLE 2. - Average Weekly Submissions by Author Type and Imputed Gender, 1 January 2018 Through 30 April 2022, Stratified by Pre- and Postpandemic Declaration on 11 March 20202. Author Type Prepandemic Declaration, Mean Postpandemic Declaration, Mean Post−Pre Difference (95% CI) First author Feminine name 6.05 5.40 −0.66 (−1.00, −0.31) Masculine name 6.23 6.70 0.47 (0.09, 0.85) Last author Feminine name 4.62 4.50 −0.12 (−0.41, 0.17) Masculine name 7.83 8.37 0.54 (0.11, 0.98) Corresponding author Feminine name 5.38 4.98 −0.40 (−0.07, −0.73) Masculine name 6.80 7.65 0.85 (0.46, 1.24) All three groups of authors showed an increase in submissions by authors with masculine names and a decrease in submissions by authors with feminine names following the declaration of the SARS-CoV-2 global pandemic. This effect was most pronounced in the first half of 2020, although it endured over the entire 111-week analysis period (eTable 1; https://links.lww.com/EDE/B998). The observed difference was largest for the first and corresponding authors (Table 2). Sixty-four percent of corresponding authors were also first authors, so these are not independent results. To visualize the changes in submissions by authors over time, we graphed all submissions by authors’ imputed gender and author type over the analysis period (1 January 2018 to 30 April 2022) (Figures 1–3). The trend lines depict weekly submissions by authors with masculine names and authors with feminine names, respectively, with kernel density smoothing of 7 days. The average weekly number of submissions pre- and postpandemic inception for authors with masculine and feminine names, respectively, has been overlaid with horizontal lines and the date of pandemic declaration has been indicated by a vertical line on each graph.FIGURE 1.: Trendline for weekly submission to Epidemiology of first authors by names’ imputed genders; 1 January 2018 through 30 April 2022, stratified by pre- and postpandemic declaration on 11 March 2020 (7-day kernel density smoothing).FIGURE 2.: Trendline for weekly submission to Epidemiology of last authors by names’ imputed gender; 1 January 2018 through 30 April 2022, divided by pre- and postpandemic declaration on 11 March 2020 (7-day kernel density smoothing).FIGURE 3.: Trendline for weekly submission to Epidemiology of corresponding authors by names’ imputed gender; 1 January 2018 through 30 April 2022, divided by pre- and postpandemic declaration on 11 March 2020 (7-day kernel density smoothing).Given earlier studies of gender differences in scientific productivity cited above, we expected to observe a difference by imputed gender in the number of submissions after the pandemic declaration. We observed that difference, concentrated primarily among the first author submissions. First authors are often earlier in their career than the last authors, suggesting that the gendered pandemic consequences affected early-career scientists more substantially than later-career scientists. We expected to observe a decline in the number of submissions for authors regardless of gender, but instead observed an increase in submissions for authors with imputed masculine names and a decrease in the number of submissions for authors with imputed feminine names. This finding suggests the possibility of a transfer of productivity from women to men, which would further amplify the gendered pandemic inequities. These and other data6,7 emphasize the disparate career impact of pandemic mitigation on the careers of women scientists, and especially those early in their careers. We recognize that our results provide only one metric of productivity and a pandemic impact for a niche group in society, but one nonetheless relevant for our readers. The observed inequity in author gender may be due to factors such as gendered differences in assumptions of escalated domestic responsibilities during lockdowns and disrupted childcare or school schedules. These differences may have varied in different societies and at different times, depending on the government response and sources of social support. The reasons why there were gendered differences in submission are beyond the scope of this report; at the journal, we observe only the articles submitted and not the projects that form the foundation for these articles. It is not possible for us to discern the pandemic impact on the conduct of the projects themselves, such as any sudden halting of work or handing off article development tasks to team members with more availability or flexibility during the pandemic. We observed an overall reduction of 0.2 articles for authors per year, which is likely more impactful to those earlier in their career, as it represents a larger proportion of their existing body of work compared with later-career researchers. Those earlier in their careers may have had fewer resources to keep projects moving, yet increased responsibilities and thus disruption because of the lack of social structures (childcare, school, dedicated at-home office space), while those later in their careers may have had more flexibility at home and more experience or resources to keep projects moving. Among our community of researchers, these results can provide some context when evaluating career productivity during the initial pandemic years. Yet, the consequences of the pandemic, like the virus itself, still linger. This pandemic has been devastating on many levels, and severe consequences were highly differential by the level of socioeconomic privilege. Our results add a relatively narrow and niche data point to this list of differential consequences, documenting how in the world of epidemiologic research and publishing, effects of the pandemic on work productivity differed by gender. Recently, life has resumed to something closer to the prepandemic era for most of the world, but not everyone has been or will be able to make up for lost time in terms of work productivity. It seems unlikely that someone who had to scale back on research projects and returned at full capacity in 2022 will ever truly compare with their counterfactual self who did not have to scale back and continued at full capacity. We encourage our community to keep these impacts in mind going forward in evaluations of readiness for promotion and other recognitions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".