Prevalence and socioeconomic characteristics of alcohol disorders among men and women in the Eastern Cape Province, South Africa
Bibliographic record
Abstract
There is growing concern about alcohol problems in low- and middle-income countries. More research is required, particularly among the younger generation. The aim of this study was to investigate the prevalence of alcohol disorders and associated socioeconomic characteristics among young men and women living in the Eastern Cape Province, South Africa. This was a cross-sectional population-based study of 977 participants (52% male and 48% female) aged 18-40, the majority of whom lived in low-income areas. Data collection was carried out in 2012 by trained fieldworkers. The Mini International Neuropsychiatric Interview (DSM-IV) was used to investigate the prevalence of alcohol dependence (increased tolerance to alcohol, failed attempt to cut down, risk of physical and mental effects) and alcohol abuse (harmful use, consistent intoxication, risk behaviour, physically hazardous, social problems). A high 12-month prevalence of alcohol dependence was found (26.5% in total; 39.0% among men and 19.1% among women) as well as of alcohol abuse (9% in total; 19.0% among men and 6.0% among women). Few socioeconomic differences emerged among the men, except older men (OR 1.94, CI 1.11-3.42) and those supported by social grants (OR 2.28, CI 1.06-4.93), who presented higher odd ratios for alcohol dependence than the reference groups. Among the women, more differences emerged: women who were widowed/single (OR 2.35, CI 1.20-4.62), had no education (OR 3.41, CI 1.04-11.21), had a low income (OR 3.26, CI 1.55-6.80) and had no social support from friends when ill presented higher odd ratios (OR 1.73, CI 1.07-2.80). In the adjusted model, marital status and low income remained statistically significant. With regard to alcohol abuse, fewer socioeconomic differences emerged. Interventions need to address the early onset of alcohol misuse in order to meet both current needs and long-standing mental and physical illness.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".