COVID-19 Pandemic: Influence of Gender Identity on Stress, Anxiety, and Depression Levels in Canada
Bibliographic record
Abstract
Background: This cross-sectional study explored variation of the prevalence of perceived stress, depression and anxiety among different self-identified gender identity groups in the Canadian population during the early stages of the COVID-19 pandemic. Methods: Anxiety, depression, and stress were assessed using the Generalized Anxiety Disorder 7-item (GAD-7) scale, Patient Health Questionnaire-9 (PHQ-9), and Perceived Stress Scale (PSS) respectively. Data were analyzed using one-way analysis of variance. Results: There were 8267 respondents to the online survey; 982 (12.0%) were male-identified, 7120 (86.9%) female-identified, and 92 (1.1%) identified as a diverse gender group. Prevalence rates for clinically meaningful anxiety (333 (41.7%), 2882 (47.6%), 47 (61.0%)), depression (330 (40.2%), 2736 (44.3%), 46 (59.7%)), and stress (702 (79.6%), 5711 (86.4%), 74 (90.2%)) were highest among respondents who self-identified as “other gender” followed by female-identified and then male-identified, respectively. There were statistically significant differences between gender groups for mean scores on GAD-7 (F (2, 6929) = 18.02, p < 0.001), PHQ-9 (F (2, 191.4) = 11.17, p < 0.001), and PSS (F (2, 204.6) = 21.13, p < 0.001). Conclusions: Gender identity differences exist in terms of the prevalence and severity of anxiety, depressive, and stress symptoms during the COVID-19 pandemic. This finding highlights the importance of incorporating self-identified gender identity in medical research, clinical practice, and policy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".