FC18-02 - Cognitive distortions among online gamblers
Bibliographic record
Abstract
Addictive disorders are being increasingly influenced by technology and one of the most recent developments is for gamblers to access games via the Internet. Prevalence data show that up to 10% of the population gamble online and studies have consistently indicated that Internet gamblers are particularly susceptible to developing gambling problems. Therefore, the purpose of this study was to explore differences between Internet and non-Internet gamblers to help determine why online gamblers are more likely to have gambling problems. Three hundred and seventy four participants (143 online gamblers, 172 males) from a large Canadian university completed an online questionnaire to investigate demographic, medium-related, comorbid psychological and cognitive factors with strong empirical support for contributing to problem gambling severity. Variables that significantly differentiated Internet and non-Internet gamblers in a univariate analyses were entered into a logistic regression to predict online gambling. A test of the full model was statistically significant, correctly classifying 77% of gamblers (64% of Internet gamblers and 85% of non-Internet gamblers). Cognitive distortions made an independent contribution to predicting Internet gamblers from those that had never wagered online. A hierarchical linear regression analysis revealed that cognitive distortions added significantly to problem gambling severity among online gamblers after controlling for other contributing variables. The findings have implications for clinicians working with Internet gamblers to specifically address thoughts related to luck, perseverance and illusion of control. As gambling technologies change and evolve, research needs to inform practice by identifying possible causal factors contributing to problem severity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".