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Record W2337224824 · doi:10.1177/0163278715616440

How Should Alcohol Problems Be Conceptualized? Causal Indicators Within the Rutgers Alcohol Problem Index

2015· article· en· W2337224824 on OpenAlexaff
Brooke J. Arterberry, Ting-Huei Chen, Álvaro Vergés, Kenneth A. Bollen, Matthew P. Martens

Bibliographic record

VenueEvaluation & the Health Professions · 2015
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversité Laval
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute on Alcohol Abuse and Alcoholism
KeywordsCausal modelStructural equation modelingLatent variableAlcoholPsychologyConstruct (python library)Index (typography)EconometricsSocial psychologyStatisticsMathematicsComputer science

Abstract

fetched live from OpenAlex

Alcohol-related problems have traditionally been conceptualized and measured by an effect indicator model. That is, it is generally assumed that observed indicators of alcohol problems are caused by a latent variable. However, there are reasons to think that this construct is more accurately conceptualized as including at least some causal indicators, in which observed indicators cause the latent variable. The present study examined the measurement model of a well-known alcohol consequences questionnaire, the Rutgers Alcohol Problem Index. Participants were 703 students from a large public university in the Northeast mandated to an alcohol intervention. We conducted a zero tetrad test to examine a measurement model consisting solely of effect indicators and a model with both causal and effect indicators. Overall, the results suggested the hybrid model fit the data better than a model with only effect indicators. These findings have implications regarding the theoretical underpinnings of alcohol-related consequences.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.637
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.314
GPT teacher head0.453
Teacher spread0.139 · 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 designQualitative
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

Citations4
Published2015
Admission routes1
Has abstractyes

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