Bibliographic record
Abstract
Background: Mental health presents a profound challenge globally, affecting around 970 million individuals. In Canada, mental disorders significantly impact the well-being of society and individuals, with one in three Canadians expected to face a mental problem during their lifetime. However, the utilization of mental health services is low, and a large proportion of the population does not have access to effective care. Objectives: This study was designed to identify key sociodemographic, clinical, and lifestyle factors associated with the prevalence of mood disorders among Canadians, thereby aiding in the development of targeted interventions. Methods: Utilizing data from the Canadian Community Health Survey 2017-2018, multivariable logistic regression analysis was performed, incorporating the sampling weights to make the inference representative of the Canadian population. Bootstrap weighting was applied to the multivariable associations to ensure robust variance estimates. Results: Females (OR, 1.98; 95% CI, 1.86-2.16) and individuals with higher obesity levels (pre-obesity: OR, 1.05; 95% CI, 1.04-1.05; obesity class 3: OR, 2.37; 95% CI, 1.88-3.00) were more likely to experience mood disorders. Conversely, higher income levels (>$80,000: OR, 0.57; 95% CI, 0.49-0.66; $60,000-$79,999: OR, 0.66; 95% CI, 0.56-0.77) and immigrant status (OR, 0.49; 95% CI, 0.43-0.56) were linked to a lower prevalence of mood disorders. Being unmarried was also associated with lower odds of mood disorders. Furthermore, additional factors such as higher education, increased life stress, and smoking were found to significantly influence the prevalence of mood disorders. Conclusions: The prevalence of mood disorders in Canada is influenced by various factors, with significant gender disparities. These findings can assist policymakers and healthcare professionals in developing targeted interventions and allocating resources effectively to meet the specific needs of at-risk populations. Future research is necessary to address these determinants.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 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) | 0.005 | 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".