Support for alcohol policies from drinkers in the City of Tshwane, South Africa: Data from the International Alcohol Control study
Bibliographic record
Abstract
INTRODUCTION AND AIMS: South Africa is considering a range of alcohol policy reforms. This study aims to determine the magnitude of public support for 13 alcohol policies in the Tshwane Metropolitan Municipality and whether this varies by demographic factors and heavy drinking status. DESIGN AND METHODS: Data are from the South African arm of the International Alcohol Control study, a household survey of adult drinkers using a multistage stratified cluster random sampling design. The sample included 1920 drinkers aged 18-65 years (62% men), with complete drinking data for 16 drinking locations on 955 persons (510 heavy and 445 not heavy drinkers). RESULTS: Over half (53%) of the sample were found to be heavy drinkers. Support varied by alcohol policy, ranging from 31% to 77%, with support above 50% for 11 of the 13 policies. Policy support was higher for policies increasing the purchase age to 21 years (77%), addressing drink driving (58-76%) and restricting physical availability (60-66%). There was slightly less support for policies restricting alcohol marketing (59%) or for policies increasing the price of alcohol (34-58%), especially if no justification was given or the funds were not earmarked. Policy support differed by age, gender, heavy drinking status and income. DISCUSSION AND CONCLUSIONS: Public support from adult drinkers for a range of alcohol policies is extensive and, as found elsewhere, was strongest for raising the minimum drinking age and lowest for increasing prices. The support from drinkers to increasing controls on alcohol could be one lever to getting control measures implemented. [Parry CDH, Trangenstein P, Lombard C, Jernigan DH, Morojele NK. Support for alcohol policies from drinkers in the City of Tshwane, South Africa: Data from the International Alcohol Control study. Drug Alcohol Rev 2017;00:000-000].
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".