Gambling Increases Self-Control Strength in Problem Gamblers
Bibliographic record
Abstract
In two studies it is demonstrated that, in the short-term, slot machine gambling increases self-control strength in problem gamblers. In Study 1 (N = 180), participants were randomly assigned to either play slot machines or engage in a control task (word anagrams) for 15 min. Subsequent self-control strength was measured via persistence on an impossible tracing task. Replicating Bergen et al. (J Gambl Stud, doi: 10.1007/s10899-011-9274-9 , 2011), control condition participants categorized as problem gamblers persisted for less time than did lower gambling risk participants. However, in the slot machine condition, there were no significant differences in persistence amongst participants as a function of their gambling classification. Moreover, problem gambling participants in the slot machine condition persisted at the impossible tracing task longer than did problem gambling participants in the control condition. Study 2 (N = 209) systematically replicated Study 1. All participants initially completed two tasks known to deplete self-control strength and a different control condition (math problems) was used. Study 2 results were highly similar to those of Study 1. The results of the studies have implications for the helping professions. Specifically, helping professionals should be aware that problem gamblers might seek out gambling as a means of increasing self-control strength.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".