Gender differences in authorship prior to and during the COVID-19 pandemic in research submissions to Occupational and Environmental Medicine (2017–2021)
Bibliographic record
Abstract
OBJECTIVE: To explore whether the COVID-19 pandemic has impacted productivity of female academics in the field of occupational and environmental health, by examining trends in male and female authorship of submissions during and prior to the COVID-19 pandemic in Occupational and Environmental Medicine. METHODS: Administrative data on submissions between January 2017 and November 2021 were obtained through databases held at BMJ journals. Author gender was identified using an existing algorithm based on matching names to social media accounts. The number and proportion of female and male primary (first) and senior (last) authors were examined for each quarter, and the average change in share of monthly submissions from male authors in the months since the pandemic compared with corresponding months prior to the pandemic were identified using regression models estimating least squares means. RESULTS: Among 2286 (64.7%) and 2335 (66.1%) manuscripts for which first and last author gender were identified, respectively, 49.3% of prepandemic submissions were from male first authors, increasing to 55.4% in the first year of the pandemic (difference of 6.1%, 95% CI 1.3% to 10.7%), before dropping to 46.6% from April 2021 onwards. Quarterly counts identified a large increase in submissions from male authors during the first year after the onset of the pandemic, and a smaller increase from female authors. The proportion of male last authors did not change significantly during the pandemic. CONCLUSIONS: These findings suggest that there has been an increase in male productivity during the COVID-19 pandemic within the field of occupational and environmental health research that is present to a lesser extent among women.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".