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Record W4407742885 · doi:10.2196/64720

The Color of Drinking Survey Questionnaire for Measuring the Secondhand Impacts of High-Risk Drinking in College Settings: Validation Study

2025· article· en· W4407742885 on OpenAlexvenueno aff
Agustina Marconi, Reonda Washington, Amanda Jovaag, Courtney Blomme, Ashley Knobeloch, Vilma Irazola, C. Cortés, Laura Gutiérrez, Natalia Elorriaga

Bibliographic record

VenueInteractive Journal of Medical Research · 2025
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintEnvironmental healthSurvey researchSecondhand smokePanel surveyPsychologyMedicineApplied psychologySocioeconomicsComputer scienceSociology

Abstract

fetched live from OpenAlex

Background: The "Color of Drinking" is a study conducted at the University of Wisconsin-Madison. It examines the secondhand harms of high-risk drinking on college students of color and explores the connection between alcohol use and the campus racial climate. Since its findings were released in 2018, this study has received significant attention from other college settings around the country. Objective: This study aims to describe the development of the most recent version of the Color of Drinking questionnaire and to assess its internal consistency, test-retest reliability, and construct validity in a sample of undergraduate students attending the University of Wisconsin-Madison. Methods: This is an observational, analytic study. Questionnaire design experts revised the original instrument, and in-depth cognitive interviews with students were conducted to evaluate comprehensibility and acceptability. The revised questionnaire was administered 2 times, 3 to 4 weeks apart, in a sample of undergraduate students. The following properties were studied: internal consistency in 4 sets of items (Cronbach α), test-retest reliability among closed-ended questions (κ statistics and intraclass correlation coefficient), and construct validity (associations with other validated instruments, such as the Alcohol Use Disorders Identification Test). For a section of questions showing low reliability, the answers to open questions and other in-depth interviews were carried out, and online surveys were conducted with another sample of undergraduate students to evaluate reliability after changes. Results: Eight students participated in the in-depth interviews, 177 responses from the online survey were included for the analysis of internal consistency, 115 for test-retest reliability, and 98 for construct validity. The 4 sets of items (sections) evaluated ("impact of alcohol consumption on academics," "impact of microaggressions," "witnessing microaggressions and alcohol intoxication," and "bystanders' interventions on alcohol intoxication") presented good internal consistency (Cronbach α between 0.723 and 0.898). Most items showed moderate to substantial test-retest reliability; agreement was from 68.1% to 95.2%, and κ coefficients ranged from 0.214 to 0.8. For construct validity, correlations between the number of drinking days, the maximum number of drinks in a day and the Alcohol Use Disorders Identification Test score were moderate to high, r=0.630 (95% CI 0.533-0.719) and r=0.647 (95% CI 0.548-0.741), respectively. Due to low reliability, a section regarding "health impacts" has been redesigned, including 8 items for the personal consumption of alcohol and the consumption of others (Cronbach α 0.735 and 0.855, respectively; agreement between the first and the second time the questionnaire was administered were 83.4% and 99.1%, and most of the items with κ coefficient from 0.476 to 0.877). Conclusions: The revised version of the Color of Drinking questionnaire showed acceptable to adequate reliability and construct validity.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.060
GPT teacher head0.436
Teacher spread0.376 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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