A polarisation rather than just an increase or a decrease - Exploring different approaches to measure the impact of the COVID-19 pandemic on alcohol consumption after one year
Bibliographic record
Abstract
Background Many studies on the impact of the COVID-19 pandemic reported that significant proportions of drinkers have either increased or decreased their consumption. These diverging trends may indicate a polarisation of drinking behaviours, suggesting that the same source of stress caused some people to drink more and others to drink less, among the same segment of the population. This study aims to explore the existence of such a polarisation by using standard statistical methods to assess data on drinking behaviour from March 2021, one year after the onset of the pandemic. Methods A representative sample of 2’000 people in Switzerland were asked to retrospectively report their drinking behaviours during the year before the introduction of the measures to contain the COVID-19 pandemic (April 2019–March 2020) and during the first year of the pandemic (April 2020–March 2021), as well as multiple aspects of their living conditions. Results A polarisation of drinking behaviours was observed among many segments of the populations, particularly among young adults (15-24 years old), those with increased fear of COVID-19 for oneself or for their financial situation, and those who experienced a precarious work situation. Chi-square test and regressions models, using the absolute value of the change in drinking habits, are suitable for measuring polarisation effects. Conclusions The polarisation of drinking behaviours occurred in some segments of the population, regardless of their pre-pandemic drinking habits. However, polarisation can only be properly measured when non-linear trends are investigated.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".