Suicide ideation in Canada during the COVID-19 pandemic
Bibliographic record
Abstract
INTRODUCTION: Many Canadians report decreased mental health during the COVID-19 pandemic, and concerns have been raised about possible increases in suicide. This study investigates the pandemic's potential impact on adults' suicide ideation. METHODS: We compared self-reported suicide ideation in 2020 versus 2019 by analyzing data from the Survey on COVID-19 and Mental Health (11 September to 4 December 2020) and the 2019 Canadian Community Health Survey. Logistic regression was conducted to determine which populations were at higher risk of suicide ideation during the pandemic. RESULTS: The percentage of adults reporting suicide ideation since the pandemic began (2.44%) was not significantly different from the percentage reporting suicide ideation in the past 12 months in 2019 (2.73%). Significant differences in the prevalence of recent suicide ideation in 2020 versus 2019 also tended to be absent in the numerous sociodemographic groups we examined. Risk factors of reporting suicide ideation during the pandemic included being under 65 years, Canadian-born or a frontline worker; reporting pandemic-related income/job loss or loneliness/isolation; experiencing a lifetime highly stressful/traumatic event; and having lower household income and educational attainment. CONCLUSION: Evidence of changes in suicide ideation due to the pandemic were generally not observed in this research. Continued surveillance of suicide and risk/protective factors is needed to inform suicide prevention efforts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".