A Year Through the COVID-19 Pandemic: Deleterious Impact of Hormonal Contraception on Psychological Distress in Women
Bibliographic record
Abstract
Background Women are more at risk than men of suffering from psychological distress during disease outbreaks. Interestingly, no biological factors have been studied to explain this disparity in such contexts. Sex hormone variations induced by hormonal contraceptives (HC) have been associated with mental health vulnerabilities. However, most studies have examined current effects of HC without considering whether a chronic modulation of sex hormone levels could induce long-lasting effects that persist after HC cessation. Objectives To date, the role of HC on psychological health in women during a disease outbreak is still unknown. We aimed to investigate both current and long-term effects of HC on psychological distress throughout the COVID-19 pandemic. Method At four time points during the COVID-19 pandemic (June 2020, September 2020, December 2020, March 2021), we collected self-reported data on psychological distress, assessing symptoms of post-traumatic stress [via the Impact of Event Scale-Revised (IES-R)], symptoms of depression, anxiety, and stress [via the Depression Anxiety Stress Scales (DASS-21)]. Linear mixed models were first used to compare men (n = 49), naturally cycling women (n = 73), and women using HC (n = 32) across time. To examine long-lasting effects of HC, exploratory analyses were restricted to women, comparing current HC users (n = 32), past users (n = 56), and never users (n = 17). Results The first model revealed that women taking HC reported stable post-traumatic stress symptoms across time, compared to naturally cycling women and men who showed a significant decrease from T1 to T2. HC users also reported greater DASS-21 total scores over time. Moreover, HC users reported higher stress and anxiety symptoms than men. In the second model, results showed that past HC users had similar anxiety levels as current HC users. These two groups reported significantly more anxiety symptoms than never users. Conclusion HC users report increased distress during the pandemic relative to naturally cycling women and men. Our results also suggest a long-lasting effect of HC intake, highlighting the importance of considering both the current use of HC and its history. This could provide some insight into potential avenues for explaining why some women are prone to higher psychological distress than men.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".