Alcohol home delivery usage and its relationship to alcohol consumption in New South Wales during COVID-19
Bibliographic record
Abstract
Background: Rapid growth in the use of alcohol home delivery services, combined with increases in home drinking during COVID-19, raises potential concerns around increased consumption. This paper aims to assess the relationship between alcohol home delivery use and consumption across levels of COVID-19 restrictions in New South Wales (NSW), Australia. Methods: A 5-wave longitudinal survey of 586 NSW residents (Mage = 35; 65.3% female) conveniently sampled across 2020. Home delivery usage and the number of daily standard drinks consumed during a typical week were assessed with a survey. Logistic regression models were estimated within each wave to identify predictors of home delivery usage, and hierarchical logistic mixed effects models were estimated to predict purchase source (home delivery vs other) at the occasion level. Results: From baseline, alcohol home delivery use rose significantly during lockdown (20% to 34%), with respondents using home delivery during lockdown and the partial re-opening wave consuming significantly more than those who were not. Use of home delivery was significantly higher during lockdown and the partial re-opening amongst people who drank more heavily, with respondents aged 36 or older more likely to use delivery services in all waves except lockdown. Conclusions: Alcohol home delivery usage increased during lockdown suggesting restrictions impeding on-premise consumption coincided with an increase in home delivery. Associations between persons who drink more heavily and use of home delivery during lockdown and the partial re-opening suggest a subset of the population that may be at increased risk of harmful consumption when accessing alcohol delivery services.
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 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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".