Temporal Stability and Predictive Validity of the Regan Attitudes Toward Non-Drinkers Scale
Bibliographic record
Abstract
The Regan Attitudes Toward Non-Drinkers Scale (RANDS) is a relatively new alcohol-related measure. Findings suggest that higher scores on the RANDS (denoting stronger endorsement of negative beliefs about non-drinkers) are related to higher self-reported levels of alcohol consumption. Available evidence also suggests that the measure is factorially unidimensional and possesses good scale score reliability (α coefficients > .80) and construct validity. However, the test–retest stability and predictive validity of the RANDS have not been investigated. The current study addressed this omission by distributing the scale to 120 Irish university students at two points in time (1 to 4 weeks apart). To examine the validity of the RANDS, other measures (e.g., alcohol consumption, motives to drink alcohol, and sensation-seeking), commonly employed in studies of drinking behavior, were used. Results indicated that the intraclass correlation coefficient (ICC) for the total RANDS was substantial (.86), suggesting it is a stable measure of attitudes toward non-drinkers. Scores on the RANDS correlated significantly with self-reported alcohol consumption, binge-drinking, and motives to drink. Finally, regression analyses demonstrated that the RANDS, measured at Time 1, accounted for unique variance in risky drinking measured at Time 2.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".