Knowledge of random events and chance in people with gambling problems: an item analysis
Bibliographic record
Abstract
This paper examines the items of two scales, the Random Events Knowledge Test (REKT) and the Chance Test, and examines their relationship with problem gambling (N = 1375). Using exploratory and confirmatory factor analysis, the REKT was broken down into four sub-scales: Due to Win, Counterintuitive Nature of random chance, Odds Do Not Improve, and Biases and Wins. The Chance Test was broken down into three sub-scales: abstract Odds, Table Odds, and Chance Odds. These sub-scales were regressed onto of problem gambling severity and revealed that more knowledge about random chance on all sub-scales of the REKT and Abstract Odds from the Chance Test were negatively related to problem gambling. On the other hand, we found that higher score on the Table Odds and Chance Odds from the Chance Test were positively related to problem gambling. The results illustrate that compared to people who do not have a gambling problem, problem gamblers have a more accurate understanding of some aspects of the chances of winning specific games, but have a poorer understanding of various implications of the independence of random events. The findings suggest potential strategies for the prevention and treatment of problem gambling.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".