The role of sex and gender in the changing levels of anxiety and depression during the COVID-19 pandemic: A cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Several studies have assessed the impact of the COVID-19 pandemic on anxiety and depression, but have not focused on the role of sex and gender. This study compared changes in the levels of anxiety and depression (pre- and post-COVID) experienced by individuals of various sexes and genders. METHODS: We used a cross-sectional online survey that assessed pre- and post-COVID symptoms of anxiety (Generalized Anxiety Disorder-2) and depression (Patient Health Questionnaire-9). General linear modeling (fixed model factorial analysis of variance) was used to evaluate changes in anxiety and depression between pre- and post-pandemic periods and explore differential effects of sex and gender on those changes. RESULTS: Our study included 1847 participants from 43 countries and demonstrated a percentage increase of 57.1% and 74.2% in anxiety and depression, respectively. For the Generalized Anxiety Disorder-2 scale (maximum score 6), there was a mean increase in anxiety by sex for male, female, and other of 1.0, 1.2, and 1.4, respectively; and by gender for man, woman, and others of 0.9, 1.3, and 1.6, respectively. For the Patient Health Questionnaire-9 (maximum score 27), there was a mean increase in depressive symptoms by sex for male, female, and other of 3.6, 4.7, and 5.5 respectively; and by gender for man, woman, and others of 3.3, 4.8, and 6.5, respectively. CONCLUSION: During COVID-19, there was an increase in anxiety and depressive symptoms for all sexes and genders, with the greatest increases reported by those identifying as non-male and non-men.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".