A comparison of alcohol measures as predictors of psychological distress in the New Zealand population
Bibliographic record
Abstract
Foulds, J., Wells, J. E., Lacey, C., Adamson, S., Sellman, J. D. & Mulder, R. (2013). A comparison of alcohol measures as predictors of psychological distress in the New Zealand population. International Journal of Alcohol and Drug Research, 2(1), 59-67. doi: 10.7895/ijadr.v2i1.73 (http://dx.doi.org/10.7895/ijadr.v2i1.73) Aims: To compare alcohol consumption and alcohol problems measures as predictors of current psychological distress.Design: A household survey. Logistic regression models investigated the association between alcohol measures and high psychological distress.Setting: New Zealand population sample.Participants: 12488 adults aged 15 and over.Measures: Alcohol use was measured by the Alcohol Use Disorders Identification Test (AUDIT). The AUDIT was separated into two component factors, the first 3 items (AUDIT-C) denoting consumption and the remaining 7 items denoting problems. Psychological distress was measured using the K10, with high psychological distress defined as a score of 12 or more.Findings: A J-shaped association was found between AUDIT score and high psychological distress. High distress was present in 6.5% of the population, 10.1% of abstainers and 35.1% of those with AUDIT scores 20 and over. Excluding abstainers, scores on the AUDIT-C were only associated with an excess of high distress at very high consumption levels indicated by a score of 10 or more. On the problems factor, the percentage with high distress was 4.5% in drinkers scoring 0, 6.1% for scores 1-3, 9.4% for scores 4-7 and 24.1% for scores of 8 or more. Results from logistic regression models including both consumption and problems factors as predictors showed that problems were stronger predictors of psychological distress than was consumption.Conclusions: The association between alcohol consumption and current mental health is relatively weak, except in the presence of very heavy consumption or alcohol problems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".