Problem Gambling and its Relation to Delayed Reward Discounting: A Secondary Analysis of a Randomized Controlled Trial
Bibliographic record
Abstract
Delayed reward discounting (DRD) refers to the phenomenon where future rewards are perceived as less valuable compared to immediate rewards. The extent of this devaluation has been repeatedly linked to addictive behaviors. DRD could play an important role in the development and maintenance of problem gambling. We aim to better understand the relationship between DRD and problem gambling, examine temporal changes in DRD, and evaluate its predictive value for adherence. We conducted a secondary analysis of data from participants in the Win Back Control study (n = 345), a two-arm RCT that demonstrated effectiveness in reducing problematic gambling behavior. DRD was assessed using the Monetary Choice Questionnaire (MCQ), the severity of problem gambling was measured with the Problem Gambling Severity Index (PGSI), and gambling symptom severity was evaluated using the Gambling Symptom Assessment Scale (G-SAS). DRD is significantly positively associated with gambling symptom severity (G-SAS: b = 13.90, p = 0.002) and problem gambling severity (PGSI: b = 9.39, p < 0.001). DRD decreased significantly on a weekly basis over the three measurement time points (b = -0.001, p < 0.001). DRD could not be identified as a mediator, which should be further investigated in future studies. DRD is significantly positively associated with problem gambling severity and gambling symptom severity. DRD does not serve as a predictor of adherence. DRD decreases over the three time points along with severities of gambling behavior and gambling symptoms. However, the findings are correlational and may be affected by attrition bias.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".