Does knowing about the long-term health effects of alcohol matter? Evidence from a university sample in Lebanon
Bibliographic record
Abstract
BACKGROUND: Research linking awareness of health effects of alcohol and harmful alcohol drinking status is limited. AIMS: To investigate the association between awareness of long-term alcohol effects and alcohol use disorders. METHODS: University students aged 18-25 years (n = 1155) completed a self-filled survey. Participants were asked if they knew that alcohol causes: (1) problems in the liver; (2) cancers of the mouth, throat, oesophagus and breast; (3) damage to the heart; and (4) weakening of the immune system. Multinomial regression analyses were conducted to assess the association between awareness of long-term alcohol effects and alcohol drinking status, including Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) alcohol use disorders. RESULTS: One third (32.8%) were past-year drinkers and 18% screened positive for DSM-5 alcohol use disorders. Compared to past-year drinkers with no alcohol use disorders, non-past-year ever drinkers were twice as likely to know about the link between alcohol and cancers of the mouth, throat, oesophagus and breast. Past-year drinkers with an alcohol use disorders were less likely to know about this association. Non-past-year ever drinkers (vs past-year drinkers with no alcohol use disorders) were also 2.6 times more likely to know the alcohol liver risks. CONCLUSIONS: There is a strong inverse relationship between awareness of the effects of alcohol and harmful consumption among young people, providing preliminary support for the protective nature of knowledge on alcohol drinking status. Efforts to increase public awareness of the long-term health effects of alcohol may be useful in reducing alcohol-related harm.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".