Prevalence and Predictors of Increased and Hazardous Alcohol Consumption in a Cohort of Older South Australian Men during COVID-19 Restrictions
Bibliographic record
Abstract
Background: Increasing levels of risky alcohol consumption in older men observed in many countries, combined with trends for increased alcohol-related misuse by men during COVID, indicate a need to examine alcohol use by older men during the pandemic. Aim: To examine the prevalence and predictors of increased and hazardous alcohol consumption in older South Australian men during COVID-19 restrictions. Method: Data collected in the latest (eighth) wave of the Men Androgen Inflammation Lifestyle Environment and Stress (MAILES) cohort study were interrogated. Participants were 746 community-dwelling older men (mean age 69 years) who completed a self-report survey on mental health, coping, COVID-related worries, and alcohol consumption during pandemic restrictions. Alcohol-related items asked about changes to overall consumption (analysed as increased vs. decreased/same) and number of standard drinks per occasion (analysed as <5 drinks [not hazardous consumption] vs. 5+ drinks [hazardous]). Two hierarchical binary logistic regressions were conducted to explore predictors of increased and hazardous alcohol intake. Results: Eight percent of men reported increased alcohol intake and nine percent reported hazardous alcohol consumption during COVID-19 restrictions. Being in a younger age group (‘younger old’; OR=0.46, 95%CI=1.03, 2.28), having mild to severe depressive symptoms (OR=1.39, 95%CI=1.10, 5.05), and greater concern about becoming sick with COVID-19 (OR=1.52, 95%CI=1.03, 2.28) were predictive of increased alcohol consumption during restrictions. Younger age group (OR=0.46, 95%CI=0.34, 0.62) and greater concern about becoming sick with COVID-19 (OR=1.67, 95%CI=1.13, 2.51) were also predictive of hazardous alcohol consumption during this time. Discussion: Men participating in longitudinal health study follow-ups may be less inclined to engage in unhelpful coping behaviours such as problematic alcohol use. Clinicians should regularly screen older men for risky alcohol consumption; a particular focus on screening ‘younger old’ men, those with more significant concerns around COVID-19, and those with depression symptoms may be warranted.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".