Alcohol Consumption Patterns: A Gender Comparative Study Among High School Youth in South Africa
Bibliographic record
Abstract
AIM: The aim of this study was to examine the alcohol drinking patterns among young male and female alcohol drinkers. METHOD: Data were collected though a questionnaire from 71 grade 11 learners who expressed that they had had an alcoholic drink in the preceding month. 62% of the respondents were male and the remaining 38% was represented by female learners. The data collected was analysed using the Statistical Package for the Social Science (SPSS). RESULTS: This study showed that young people begin using alcohol at a relatively young age. Furthermore, male drinkers have an earlier alcohol debut than their female counterparts. Beer, cider and wine were the most consumed beverages, with males more inclined to drink beer and females gravitating towards drinking wine. There was an even split between ciders in the study, with the majority of both male and female respondents indicating that their drink of choice was cider. Weekends are the most opportune moments for the youth to consume alcohol. Holidays are also earmarked by the youth to engage in alcohol consumption. CONCLUSION: The results show that the age of alcohol debut is as low as 8 years for males and 10 years for females. Males have a higher prevalence of alcohol use than females. There is however no difference in binge drinking between the two gender as binge drinking and drinking to get drunk are the preferred methods of alcohol consumption for both genders.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".