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Record W4285011291 · doi:10.1037/adb0000859

Bidirectional prospective associations between behavioral economic indicators and drinking patterns during alcohol use disorder natural recovery attempts.

2022· article· en· W4285011291 on OpenAlexaboutno aff
JeeWon Cheong, Jillian M. Rung, Jalie A. Tucker

Bibliographic record

VenuePsychology of Addictive Behaviors · 2022
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
FundersNational Institute on Alcohol Abuse and AlcoholismNational Institutes of Health
KeywordsQuarter (Canadian coin)PsychologyDiscountingPreferenceDelay discountingTime preferenceDevelopmental psychologyEconomicsImpulsivityMicroeconomics

Abstract

fetched live from OpenAlex

OBJECTIVES: Behavioral economic (BE) theory posits that harmful alcohol use is a joint product of elevated alcohol demand and preference for immediate over delayed rewards. Despite cross-sectional research support, whether expected bidirectional relations exist between BE indicators and drinking during recovery attempts is unknown. Therefore, this prospective research investigated quarter-by-quarter cross-lagged associations between BE simulation tasks and drinking following a natural recovery attempt. Higher demand and discounting in a given quarter should predict subsequent drinking. Conversely, drinking in a given quarter should predict subsequent higher demand and discounting. METHOD: , elasticity) were assessed at baseline and 3-, 6-, 9-, and 12-month follow-ups. Longitudinal cross-lagged models related each BE indicator in the previous quarter to drinking status in the next quarter, and vice versa. RESULTS: s < .05). Hypothesized associations for other BE indices were inconsistent or partially supported. CONCLUSIONS: Alcohol purchase task metrics showed some hypothesized prospective associations with drinking during a natural recovery attempt, which supports their ecological validity as relapse risk indicators. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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 score0.003
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.029
GPT teacher head0.334
Teacher spread0.305 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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