Alcohol access and purchasing behaviour during <scp>COVID</scp>‐19 pandemic restrictions: An exploration of sociodemographic, health and psychosocial correlates in two Canadian provinces
Bibliographic record
Abstract
INTRODUCTION: Alcohol access has changed in Canada during the COVID-19 pandemic. This study compares the use of two novel purchasing modes (via delivery and in excess/stockpiling) to traditional, in-person purchasing to determine if their use is associated with sociodemographic and psychosocial characteristics, perceived COVID-19 health risk or consumption frequency. METHODS: We draw on cross-sectional survey data from 2000 residents of the Canadian provinces of Nova Scotia and New Brunswick, ages 19 years and older that were administered online and by telephone. Associations between purchasing modes and sociodemographic and psychosocial characteristics, perceived COVID-19 health risk or consumption frequency were assessed using logistic regression. RESULTS: About 70% of people who drink purchased in-person, 17% used delivery and 30% purchased in excess/stockpiled. Sociodemographic and psychosocial attributes varied among those at increased odds of using each purchasing mode. Those at higher COVID-19 health risk were at higher odds of getting alcohol delivered. Increased drinking frequency was associated with alcohol delivery and purchasing in excess/stockpiling. DISCUSSION AND CONCLUSIONS: This study highlights the need for increased public health considerations regarding changes to alcohol regulations. Alcohol delivery and purchasing in excess/stockpiling is positively associated with heavier drinking. Drinkers at higher COVID-19 health risk were more likely to purchase online for delivery, which suggests novel purchasing modes may serve a partial public health function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".