Mood, motives, and money: An examination of factors that differentiate online and non-online young adult gamblers
Bibliographic record
Abstract
Background and aims To date, there is a lack of research on psychological factors associated with young adult online gambling. The current study examined differences between young adult online and non-online gamblers, using information gathered at baseline and over 30 days during which participants reported on their moods, gambling behaviors, and reasons for initiating and discontinuing gambling. Methods Participants were 108 young adult regular gamblers (i.e., gambling four or more times in the past month) who participated in a 30-day daily diary study. Results Male gender, baseline coping motives for gambling and negative affect averaged across the 30 days emerged as significant correlates of online gambling, over and above other background variables. Online gamblers also scored higher on a baseline measure of pathological gambling. Over the 30 days of self-monitoring, online gamblers spent more time gambling, and won more money gambling, whereas non-online gamblers consumed more alcohol while gambling. Online gambling was more often initiated to make money, because of boredom and to demonstrate skills, whereas non-online gambling was more often initiated for social reasons and for excitement. Online gambling was more often discontinued because of boredom, fatigue or distress, whereas non-online gambling was discontinued because friends stopped gambling or mood was improved. Discussion and conclusions This study provides preliminary evidence that coping strategies may be particularly important to reduce risks for online gamblers, whereas strategies for non-online gamblers should focus on the social aspects of gambling.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".