Characteristics and Predictors of Heavy Episodic Drinking (HED) among Young People Aged 16–25: The International Alcohol Control Study (IAC), Tshwane, South Africa
Bibliographic record
Abstract
In South Africa, little is known about alcohol consumption patterns, such as drinks consumed, container size, salience of alcohol price, affordability and availability, and perceptions of alcohol policies as potential predictors of heavy episodic alcohol (HED) use among young people. This paper examines predictors of HED among young people with specific consideration given to these alcohol consumption patterns. This study conducted in the Tshwane Metropole in 2014 employed multi-stage stratified cluster random sampling. Participants were between the ages 16–25 years. A structured questionnaire was used to collect data. Of the 287 (n = 678) participants who had used alcohol in the past six months and for whom we had complete consumption data, almost half were identified as heavy episodic drinkers (HEDs) and were significantly more likely to consume alcohol on a daily basis (p = 0.001). Having nightclub as the primary drinking location (p = 0.023) and drinking from a container size bigger than one standard drink (p = 0.014) were significant predictors for HED. HEDs were also more likely to have a perception that most people consume alcohol (p = 0.047). The results point to HED of alcohol among young people who drink in South Africa, highlighting the need for multicomponent interventions.
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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 | 0.002 |
| 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".