Heavy episodic drinking and self-reported increased alcohol use during the COVID-19 pandemic: a spotlight on frontline and essential workers in Canada
Bibliographic record
Abstract
INTRODUCTION: There is evidence that some frontline and essential workers have increased their alcohol use during the COVID-19 pandemic; however, this has not been examined in Canada. METHODS: Using the Survey on COVID-19 and Mental Health 2020, weighted prevalence and 95% confidence intervals of self-reported increased alcohol consumption and heavy episodic drinking were calculated for each of the population groups: frontline workers, essential workers, and nonfrontline or essential workers (NFEW). Logistic regression was used to examine the associations between social determinants of health, mental health and alcohol use for each group. RESULTS: The prevalence of increased alcohol consumption and past-month heavy episodic drinking did not differ across frontline workers, essential workers and NFEW. For the three groups, nonracialized group members had significantly higher odds for both outcomes. Screening positive for either generalized anxiety disorder or mood disorder was significantly associated with increased alcohol consumption across the three groups. For frontline and essential workers, females had significantly lower odds of heavy episodic drinking compared to males. For essential workers only, living in a rural area was significantly associated with lower odds of increased alcohol use, and screening positive for posttraumatic stress disorder was significantly associated with increased odds of heavy episodic drinking. For frontline workers only, living in a rural area was significantly associated with lower odds of heavy episodic drinking. CONCLUSION: While frontline and essential workers were not more likely to report increased alcohol consumption and heavy episodic drinking compared to NFEW, there were some differences in factors associated with alcohol use. Such findings demonstrate the benefit of examining each group separately to provide information for targeted prevention strategies.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".