Predictors of reported alcohol intake during the first and second waves of the COVID-19 pandemic in Canada among middle-aged and older adults: results from the Canadian Longitudinal Study on Aging (CLSA)
Bibliographic record
Abstract
OBJECTIVE: To examine proportions and predictors of change in alcohol intake and binge drinking during the first 2 waves of the COVID-19 pandemic among middle-aged and older participants in the Canadian Longitudinal Study on Aging (CLSA) COVID-19 Questionnaire Study. METHODS: A total of 28,559 (67.2% of the potential sample) CLSA participants consented to the study with 24,114 completing the exit survey (fall 2020). Descriptive statistics and logistic regressions to examine predictors of change (increase or decrease) in alcohol intake and binge drinking were performed. RESULTS: Among alcohol users, 26.3% reported a change in alcohol consumption during the first 10 months of the pandemic. Similar percentages increased (13.0%) or decreased (13.3%) consumption. In our mutually adjusted logistic regression model, odds of change in alcohol intake were greater for younger age, higher income, current cannabis smoker, positive screen for depression, anxiety, and loneliness. The magnitude of all associations for decreased intake was less than that of increased intake, and the directions were opposite for male sex and age. Predictors of current binge drinking (27.9% of alcohol users) included male sex, younger age, higher education and income, cannabis use, depression, and anxiety. CONCLUSION: Factors predictive of potentially worrisome alcohol use (i.e. increased intake, binge drinking) included younger age, sex, greater education and income, living alone, cannabis use, and worse mental health. Some of these factors were also associated with decreased intake, but the magnitudes of associations were smaller. This information may help direct screening efforts and interventions towards individuals at risk for problematic alcohol intake during the 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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".