Suicidal ideation and mental illnesses during the COVID-19 pandemic in Canada
Bibliographic record
Abstract
Abstract Background The presence of a mental illness is a known risk factor for suicide mortality and other suicide-related behaviours, including suicidal ideation. We examined prevalence of mental illnesses among adults with and without suicidal ideation in Canada during the pandemic. Data and methods We used pooled data from the 2020, 2021, & 2023 Survey on COVID-19 and Mental Health to estimate the prevalence of moderate to severe symptoms of generalized anxiety disorder, major depressive disorder, and posttraumatic stress disorder among adults who reported suicidal ideation, compared to those who did not report suicidal ideation. We also conducted the analyses across sociodemographic factors and COVID-19 pandemic-related experiences. Results Among adults in Canada who reported suicidal ideation, 83.0% also screened positive for at least one of the three mental illnesses. The prevalence of mental illnesses did not differ significantly across sociodemographic factors or pandemic-related experiences. In contrast, the prevalence of at least one mental illness among adults without suicidal ideation was much lower (21.2%), and this prevalence differed by sociodemographic factors and pandemic-related experiences. Interpretation During the COVID-19 pandemic, most adults in Canada with suicidal ideation had moderate to severe symptoms of mental illness.
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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 | 0.003 | 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".