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Record W2120309394 · doi:10.1037/1064-1297.11.2.176

Effects of negative and positive mood phrases on priming of alcohol words in young drinkers with high and low anxiety sensitivity.

2003· article· en· W2120309394 on OpenAlexaff
Martin Zack, Constantine X. Poulos, Fofo Fragopoulos, Colin M. MacLeod

Bibliographic record

VenueExperimental and Clinical Psychopharmacology · 2003
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsMoodPsychologyPriming (agriculture)AnxietyClinical psychologyAlcoholAffect (linguistics)Developmental psychologyPsychiatryCommunication

Abstract

fetched live from OpenAlex

This study investigated whether potential emotional cues for drinking activate alcohol concepts in young drinkers. Participants were 84 university freshmen with high or low levels of anxiety sensitivity (AS). A verbal priming task measured activation (i.e., priming) of alcohol concepts (e.g., beer) by positive and negative mood phrases. Time to read alcohol target words was the dependent measure. Negative mood phrases consistently primed alcohol targets; positive mood phrases did not. Degree of negative mood priming did not differ as a function of gender or AS. Reported tendency to drink in bad moods predicted negative mood priming in women, whereas men showed negative mood priming irrespective of their reported drinking tendency. A general association between negative mood priming and severity of alcohol problems also emerged.

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)0.000
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0040.000

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 head0.014
GPT teacher head0.356
Teacher spread0.342 · 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

Citations39
Published2003
Admission routes1
Has abstractyes

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