The gender peak effect: Women are most vulnerable to infections during COVID-19 peaks
Bibliographic record
Abstract
In this article, we describe a gender peak effect that women's relative share in COVID-19 infections increases when there is a sharp increase in cases, and it reaches the highest level during peak times in each wave of the COVID-19 outbreak. We demonstrate this gender peak effect by analyzing detailed, sex-disaggregated Public Health Agency of Canada (PHAC) data. The data include 1,045,998 men and women who were confirmed cases of COVID-19 from March 2020 to February 2022. We show that women's relative share in COVID-19 infections always increases and reaches the level exceeding men's share when we see a sharp peak in case number. We further show that women's higher share in care work (e.g., captured by occupation and age variables) largely explains their elevated infections during COVID-19 peaks. Effective public health interventions during infectious disease outbreaks must recognize this potential gender peak effect and take appropriate measures to curb women's health vulnerabilities.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".