Evaluating Mood and Anxiety Disorders in Canada Through a Gender-Based Analysis Plus (GBA+) Lens
Bibliographic record
Abstract
Abstract Background The prevalence of mental disorders continues to increase worldwide. We assessed the prevalence and associated factors of mood and anxiety disorders in the Canadian population using a Gender-Based Analysis Plus (GBA+) lens on a nationally representative survey. Methods A secondary analysis of the 2017-2018 Canadian Community Health Survey (CCHS) - Annual Component was conducted using a GBA+ lens, an analytical process to incorporate sex, gender, and other intersecting identity factors into research, programs and policies. Sampling and bootstrap weights were applied to account for complex sampling design. Chi-square test and multivariable logistic regression models were used to assess associated factors of mood and anxiety disorders. Results 2017-2018 CCHS included 113,290 observations, representative of 98% of the Canadian population over the age of 12. Mood and anxiety disorders were more prevalent among females than males (11.0% vs 6.4% and 11.6% vs 6.3% respectively, p < 0.001). Logistic regression analyses revealed higher odds for both disorders for those who were female, unemployed, smokers, homosexual or bisexual, had low education and income levels, suffered from food insecurity or had disabilities. Statistically significant interactions were observed between sex and factors such as age, income, employment and sexual orientation. Of those who had a mood or anxiety disorder, 5.0% reported having unmet mental health care needs compared to 1.0% of the general population (p < 0.001). Those reporting unmet mental health care needs were more frequently younger, females, single parents, with disabilities, lower income and food insecurity. Conclusions Females in Canada continue to be affected by mood and anxiety disorders at higher rates than males. Strategies for preventing mental health disorders and improving mental health care must be tailored towards the needs of specific groups. We recommend the use of GBA+ as a guide in both research and policymaking. Key messages • This study provides estimates of the present state of mood and anxiety disorders in the Canadian population using secondary data from a cross-sectional, nationwide survey conducted through 2017-2018. • The application of Gender-Based Analysis Plus (GBA+) helps with the systematic evaluation of healthcare disparities and development of targeted strategies to address gaps in mental health.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".