On‐ and off‐licensed premises drinking behaviour among adults in the city of Tshwane, South Africa: An analysis of data from the International Alcohol Control study
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Alcohol use is among the leading risk factors for premature death and morbidity in South Africa. This study sought to identify factors associated with drinking behaviour in on- and off-licensed premises (typical occasion quantity and frequency) among adults in the City of Tshwane, South Africa. DESIGN AND METHODS: A household survey was conducted in 2014, using a multi-stage stratified cluster random sampling design. Participants comprised 982 adults (65% males) aged 18-65 years. Factors explored included socio-demographic variables and situational variables at on- and off-licensed premises. Multiple logistic regression analysis was performed to predict drinking behaviour at on- and off-licensed premises. RESULTS: The majority of the participants consumed alcohol in off-licensed premises (64% vs. 36%). However, participants who consumed alcohol at on-licensed premises were more likely to drink more alcohol and more frequently (weekly). Additionally, participants who consumed alcohol in above-average sized containers were more likely to consume six or more drinks and drink weekly. Being of high socio-economic status was associated with drinking weekly at off-licensed premises, while being less educated was associated with a significantly higher frequency of drinking at on-licensed premises. DISCUSSION AND CONCLUSIONS: Interventions to reduce alcohol use should target specific drinking behaviour at on- and off-licensed premises, for example, regulating the availability of alcohol in big-sized containers and the need for cutting down on quantity of alcohol and frequency of drinking for South African males who drink at on- and off-licensed premises.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".