Associations between adherence to public health measures and changes in alcohol consumption among middle-aged and older adults during the COVID-19 pandemic: the Canadian Longitudinal Study on Aging (CLSA)
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic and associated public health measures (PHMs) potentially affected alcohol consumption. Our objectives were to evaluate if adherence to PHMs was associated with changes in alcohol consumption and binge drinking during the COVID-19 pandemic. METHODS: A prospective cohort study was conducted with participants (50-96 years) in the Canadian Longitudinal Study on Aging (N = 23 615). Adjusted odds ratios (aORs) were estimated from multinomial logistic regression models for associations between PHM adherence (self-quarantine, attending public gatherings, leaving home, mask wearing and handwashing) and self-reported changes in alcohol consumption during the first year of the pandemic and prospectively measured changes in alcohol consumption frequency and frequency of binge-drinking events from 2015-2018 to 2020. RESULTS: During the first year of the pandemic, 13% (n = 2733) of participants self-reported increased alcohol consumption, while 13% (n = 2921) self-reported decreased consumption. Prospective measures suggested 19.1% (n = 4421) increased and 34.5% (n = 7971) decreased consumption frequency, while 12.9% (n = 1427) increased and 17.6% (n = 1953) decreased frequency of binge-drinking events. High PHM adherence, compared to low, was associated with higher odds of decreased alcohol consumption frequency (aOR = 1.17; 95% confidence interval [CI]: 1.06-1.30). No associations were observed between PHM adherence and self-reported change in alcohol consumption or frequency of binge-drinking events. Associations were consistent across socioeconomic groups. CONCLUSION: PHM adherence was associated with decreased, and not increased, frequency of alcohol consumption by adults aged 50-96 years in the first year of the COVID-19 pandemic.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".