Higher Perceived Stress during the COVID-19 pandemic increased Menstrual Dysregulation and Menopause Symptoms
Bibliographic record
Abstract
Abstract Objective The increased stress the globe has experienced with the COVID-19 pandemic has affected mental health, disproportionately affecting women. However, how perceived stress in the first year affected menstrual and menopausal symptoms has not yet been investigated. Methods Residents in British Columbia, Canada, were surveyed online as part of the COVID-19 Rapid Evidence Study of a Provincial Population-Based Cohort for Gender and Sex (RESPPONSE). A subgroup (n=4171) who were assigned female sex at birth (age 25-69) and were surveyed within the first 6-12 months of the pandemic (August 2020-February 2021), prior to the widespread rollout of vaccines, were retrospectively asked if they noticed changes in their menstrual or menopausal symptoms, as well as completing validated measures of stress, depression, and anxiety. Results We found that 27.8% reported menstrual cycle disturbances and 6.7% reported increased menopause symptoms. Those who scored higher on perceived stress, depression, and anxiety scales were more likely to have reproductive cycle disturbances. Free text responses revealed that reasons for disturbances were perceived to be related to the pandemic. Conclusions The COVID-19 pandemic has highlighted the need to research women’s health issues, such as menstruation. Our data indicates that in the first year of the pandemic, almost a third of the menstruating population reported disturbances in their cycle, which is approximately two times higher than in non-pandemic situations and four times higher than any reported changes in menopausal symptoms across that first year of the pandemic. Summary Sentences Women+ with higher anxiety, depression or perceived stress scores during the first year of the pandemic were more likely to have experienced menstrual cycle phase disturbance or menopausal status disruption. Younger women were particularly prone to disturbances in their reproductive cycles.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".