Heavy drinking and contextual risk factors among adults in South Africa: findings from the International Alcohol Control study
Bibliographic record
Abstract
BACKGROUND: There is limited information about the potential individual-level and contextual drivers of heavy drinking in South Africa. This study aimed to identify risk factors for heavy drinking in Tshwane, South Africa. METHODS: A household survey using a multi-stage stratified cluster random sampling design. Complete consumption and income data were available on 713 adults. Heavy drinking was defined as consuming ≥120 ml (96 g) of absolute alcohol (AA) for men and ≥ 90 ml (72 g) AA for women at any location at least monthly. RESULTS: 53% of the sample were heavy drinkers. Bivariate analyses revealed that heavy drinking differed by marital status, primary drinking location, and container size. Using simple logistic regression, only cider consumption was found to lower the odds of heavy drinking. Persons who primarily drank in someone else's home, nightclubs, and sports clubs had increased odds of heavy drinking. Using multiple logistic regression and adjusting for marital status and primary container size, single persons were found to have substantially higher odds of heavy drinking. Persons who drank their primary beverage from above average-sized containers at their primary location had 7.9 times the odds of heavy drinking as compared to persons who drank from average-sized containers. Some significant associations between heavy drinking and age, race, and income were found for certain beverages. CONCLUSION: Rates of heavy drinking were higher than expected giving impetus to various alcohol policy reforms under consideration in South Africa. Better labeling of the alcohol content of different containers is needed together with limiting production, marketing and serving of alcohol in large containers.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".