Mood, motives, and gambling in young adults: An examination of within- and between-person variations using experience sampling.
Bibliographic record
Abstract
It is well established that young adults are a population at risk for problem gambling and that young adults gamble for various reasons, including positive mood enhancement and negative mood reduction. Although these motives have been identified as important proximal predictors of gambling, the research to date has focused on between-subjects relationships. What is missing is a process-level understanding of the specific within-subjects relations between mood-regulation motives for gambling, mood states, and gambling behaviors. The current study used experience sampling to assess the specific link between gambling motives, mood states, and gambling behavior. Participants were 108 young adults (ages 19-24 years), who completed baseline measures of gambling motives and gambling problems and then reported on their mood states and gambling behavior three times a day for 30 days. Multilevel modeling analyses revealed a significant positive moderating effect for enhancement motives on the relationship between positive mood and amount of time spent gambling and number of drinks consumed while gambling. In addition, problem gambling status was associated with consuming fewer drinks while gambling at higher levels of positive mood, and spending more money than intended at higher levels of negative mood. Unexpectedly, there was only one moderating effect for coping motives on the mood-gambling relationship; low coping motivated gamblers consumed more alcohol while gambling at higher levels of positive mood, whereas high coping motivated gamblers did not change their drinking in response to positive mood. The current findings highlight enhancement motives as risky motives for young adult gambling, particularly in the context of positive mood, and suggest that gambling interventions should include strategies to address positive mood management.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".