Assessing consequences of alcohol and drug abuse in a drinking driving population
Bibliographic record
Abstract
The experience of negative consequences of use is often considered to be a critical dimension of the addition process, however relatively few assessment instruments focus solely on this dimension. This is especially true in the convicted driving under the influence (DUI) population, where many have by definition experienced negative consequences of use. The Adverse Consequences of Substance Use Scale (ACSUS) is a brief (8 item), clinically-based instrument that measures the problems resulting from substance use. In the current investigation the psychometric characteristics of this scale was examined using data from a large sample of convicted drink-drivers (n = 5409) and it was then compared to two other scales (the Alcohol Dependence Scales and the Research Institute on Addictions Self-inventory). Cronbach's alpha for the ACSUS was 0.726 and inter-item and item-total correlations were within the acceptable range. Factor analysis revealed a one factor solution accounting for 37% of the variance. Concurrent validity was demonstrated by good correlation with the RIASI (r = 0.416) and the ADS (r = 0.510), as well as several other substance use quantity and frequency measures. The ACSUS was also found to discriminate significantly between clinically distinct populations such as first and multiple offenders and binge drinkers. When compared to the RIASI and ADS using Fisher's z-transformation, the ACSUS had significantly higher correlations with measures of substance use treatment seeking and legal involvement. The utility and applicability of this measure is discussed.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".