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Record W2606560497 · doi:10.1177/2158244017698728

Temporal Stability and Predictive Validity of the Regan Attitudes Toward Non-Drinkers Scale

2017· article· en· W2606560497 on OpenAlexaff
Daniel Regan, Todd G. Morrison

Bibliographic record

VenueSAGE Open · 2017
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPredictive validityPsychologyScale (ratio)Test validitySocial psychologyEconometricsStatisticsPsychometricsDevelopmental psychologyMathematicsGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.237

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.084
GPT teacher head0.344
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2017
Admission routes1
Has abstractyes

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