Increased alcohol use, heavy episodic drinking, and suicide ideation during the COVID-19 pandemic in Canada
Bibliographic record
Abstract
OBJECTIVE: Alcohol use is a known risk factor for suicidality, yet this relationship has not been explored during the pandemic in Canada. As a growing body of evidence demonstrates the negative impact of COVID-19 on alcohol consumption and associated harms in Canada, there is a need to examine this more closely. METHODS: Using the Survey on COVID-19 and Mental Health 2020, we compared the prevalence of suicide ideation among: (1) individuals who reported an increase in alcohol consumption vs those who reported a decrease/no change, and (2) individuals who reported past month heavy episodic drinking vs those who did not. We compared overall unadjusted odds ratios and across a number of sociodemographic and mental health variables. All estimates were weighted to ensure they were nationally representative. RESULTS: The prevalence and likelihood of suicide ideation were significantly higher among people who reported increased alcohol consumption during the pandemic (4.9% vs 2.0%; OR = 2.6, 95% CI: 1.8, 3.7) and people who reported past month heavy episodic drinking (3.4% vs 2.1%; OR = 1.7, 95% CI: 1.2, 2.3). Males and middle-aged and older-aged individuals had the highest odds ratios for increased alcohol consumption and past month heavy episodic drinking with suicide ideation. CONCLUSION: In the Canadian general population during the COVID-19 pandemic, there were significant associations between suicide ideation and increased alcohol use as well as past month heavy episodic drinking across specific sociodemographic subgroups. Future research could explore these associations while adjusting for social determinants of health such as income security, employment, education, social support, stress, and 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".