Bidirectional prospective associations between behavioral economic indicators and drinking patterns during alcohol use disorder natural recovery attempts.
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".