Bibliographic record
Abstract
According to the 2021 Worldwide Gambling Statistics website, more than a quarter of the population gambles, which means literally billions of people gamble at least once a year (Casino.org, 2021). However, despite the vast number of people gambling per year, there is a lack of research on how emotion regulation affects their perceptions and experiences of gambling. Thus, the aim of this study was to better understand the role of emotion regulation deficits in gambling. A survey was conducted to assess the relationship between frequency and type of gambling behavior and emotion regulation difficulties. The participants were gathered from the UCR Psychology Subject Pool (N = 195; after attention checks, N = 162). These participants were directed to a survey that assessed personal experiences and beliefs about gambling and their emotion regulation strategies and difficulties. Results from correlational analyses indicated that people who tend to use cognitive reappraisal (thinking differently to change their emotions), but not expressive suppression (hiding their emotions), gambled in a more controlled way. Suppression tendencies did not predict any gambling experience or belief. In addition, people who generally had greater difficulty regulating their emotions reported gambling less frequently and gambling in a more enjoyable and focused but also stressful way. The findings suggest that cognitive reappraisal may provide a benefit for individuals who gamble in moderation. Furthermore, those who struggle with regulating their emotions may experience gambling in different ways compared to those who struggle less with emotion regulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".