Steep delay discounting and addictive behavior: a meta-analysis of continuous associations
Bibliographic record
Abstract
AIMS: To synthesize continuous associations between delayed reward discounting (DRD) and both addiction severity and quantity-frequency (QF); to examine moderators of these relationships; and to investigate publication bias. METHODS: Meta-analysis of published studies examining continuous associations between DRD and addictive behaviors. Published, peer-reviewed studies on addictive behaviors (alcohol, tobacco, cannabis, stimulants, opiates and gambling) were identified via PubMed, MEDLINE and PsycInfo. Studies were restricted to DRD measures of monetary gains. Random-effects meta-analysis was conducted using Pearson's r as the effect size. Publication bias was evaluated using fail-safe N, Begg-Mazumdar and Egger's tests, meta-regression of publication year and effect size and imputation of missing studies. RESULTS: ). Significantly larger effect sizes were observed for studies examining severity compared with QF (P = 0.01), but not between the type of addictive behavior (P = 0.30) or DRD assessment (P = 0.90). Indices of publication bias suggested a modest impact of unpublished findings. CONCLUSIONS: Delayed reward discounting is associated robustly with continuous measures of addiction severity and quantity-frequency. This relation is generally robust across type of addictive behavior and delayed reward discounting assessment modality.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.022 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".