Knowledge and beliefs about alcohol consumption, longer-term health risks, and the link with cancer in a sample of Australian adults
Bibliographic record
Abstract
AIM: This study explores knowledge and beliefs about longer-term health risks related to alcohol consumption among Australian adults. METHODS: Data were drawn from the 2009 Cancer Institute NSW Lifestyle and Cancer Survey, a telephone survey of adults in NSW. Participants (n=1255) were asked about their alcohol consumption, knowledge of the Australian guidelines (revised in 2009), and personal perceptions and beliefs about longer-term health risks from alcohol consumption. RESULTS: Seventy-eight percent of the sample drank alcohol either occasionally or weekly, with 37% of drinkers drinking above the current Australian guidelines (two standard drinks on any day). Two-thirds (67%) correctly nominated the maximum number of standard drinks per day that met the current Australian guidelines, and a similar proportion (64%) agreed that regular moderate alcohol consumption can have serious health consequences in the longer term. Knowledge of the guidelines and longer-term health consequences was lower for drinkers, especially those drinking above the guidelines. Less than half (48%) of the participants were aware that drinking alcohol could cause cancer and 51% were aware that limiting alcohol intake helps prevent cancer. CONCLUSION: The current Australian guidelines, the longer-term health risks and the link with cancer are not well understood, especially by those who drink frequently and above the guidelines.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".