Changes in Alcohol Habits Among Workers During the Confinement of COVID-19: Results of a Canadian Cross-Sectional Survey
Bibliographic record
Abstract
Background: The restrictions implemented around the world to contain the spread of the coronavirus disease 2019 (COVID-19) impact workers. Emotional distress and maladaptive behaviors such as alcohol misuse are expected, particularly in vulnerable groups such as front-line health workers. In the present study, we examined if alcohol consumption behaviors in Quebec workers changed during confinement of the COVID-19 pandemic, and whether healthcare workers reported specific patterns of changes. Methods: Data were obtained from an anonymous online survey conducted among adult workers aged ⩾18 years in the province of Quebec, Canada, between May 25, 2020 and June 26, 2020. Participants provided self-reported data regarding sociodemographic including field of work, as well as mental health disorders, alcohol use, alcohol craving, and type of alcohol consumed. Changes in alcohol behaviors were assessed using Wilcoxon signed rank test for categorial variables and paired- t tests for continuous variables. Results: The survey was completed by 847 participants (77.8% women), with 42.5% healthcare workers. Participants reported increased daily alcohol use ( Z = −10.60; P < .001, r = −.372) and alcohol craving ( P < .001, d = 0.485) during the confinement. Only the type of alcohol consumed during the confinement differed between health care workers and other workers (OR = 0.45, P = .003). Health care workers used less high alcohol products during the confinement. Conclusion: Our results show a significant increase in daily alcohol consumption and in alcohol craving during the confinement in the Quebec working population.
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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 | 0.000 |
| 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".