Higher perceived stress during the COVID-19 pandemic increased menstrual dysregulation and menopause symptoms
Bibliographic record
Abstract
BACKGROUND: The increased stress the world experienced with the coronavirus disease (COVID-19) pandemic affected mental health, disproportionately affecting females. However, how perceived stress in the first year affected menstrual and menopausal symptoms has not yet been investigated. OBJECTIVES: This study evaluates the effect that the first year of the COVID-19 pandemic had on female reproductive and mental health. 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. A subgroup of participants (n = 4171), who were assigned female sex at birth (age 25-69 years) and were surveyed within the first 6-12 months of the pandemic (August 2020-February 2021), prior to the widespread rollout of vaccines, was retrospectively asked if they noticed changes in their menstrual or menopausal symptoms, and completing validated measures of stress, depression and anxiety. DESIGN: This is a population-based online retrospective survey. 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 report reproductive cycle disturbances. Free-text responses revealed that reasons for disturbances were perceived to be related to the pandemic. CONCLUSION: The COVID-19 pandemic has highlighted the need to research female-specific health issues, such as menstruation. Our data indicate that in the first year of the pandemic, almost one-third of the menstruating population reported disturbances in their cycle, which was related to percieved stress, depression and anxiety scores.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".