Pandemic-related impacts and suicidal ideation among adults in Canada: a population-based cross-sectional study
Bibliographic record
Abstract
INTRODUCTION: Recent evidence has suggested an increase in suicidal ideation during the COVID-19 pandemic. Our objectives were to estimate the likelihood of suicidal ideation among adults in Canada who experienced pandemic-related impacts and to determine if this likelihood changed during the pandemic. METHODS: We analyzed pooled data for 18 936 adults 18 years or older who responded to two cycles of the Survey on COVID-19 and Mental Health collected from 11 September to 4 December 2020 and from 1 February to 7 May 2021. We estimated the prevalence of suicidal ideation since the pandemic began and conducted logistic regression to evaluate the likelihood of suicidal ideation by adults who experienced pandemic-related impacts, and by factors related to social risk, mental health status, positive mental health indicators and coping strategies. RESULTS: Adults who had adverse pandemic-related experiences were significantly more likely to experience suicidal ideation; a dose-response relationship was evident. People who increased their alcohol or cannabis use, expressed concerns about violence in their home or who had moderate to severe symptoms of depression, anxiety or posttraumatic stress disorder also had significantly higher risk of suicidal ideation. The risk was significantly lower among people who reported high self-rated mental health, community belonging or life satisfaction, who exercised for their mental and/or physical health or who pursued hobbies. CONCLUSION: The COVID-19 pandemic has influenced suicidal ideation in Canada. Our study provides evidence for targeted public health interventions related to suicide prevention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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".