Gambling-Related Cognition Scale (GRCS): Are skills-based games at a disadvantage?
Bibliographic record
Abstract
The Gambling-Related Cognition Scale (GRCS; Raylu & Oei, 2004) was developed to evaluate gambling-related cognitive distortions for all types of gamblers, regardless of their gambling activities (poker, slot machine, etc.). It is therefore imperative to ascertain the validity of its interpretation across different types of gamblers; however, some skills-related items endorsed by players could be interpreted as a cognitive distortion despite the fact that they play skills-related games. Using an intergroup (168 poker players and 73 video lottery terminal [VLT] players) differential item functioning (DIF) analysis, this study examined the possible manifestation of item biases associated with the GRCS. DIF was analyzed with ordinal logistic regressions (OLRs) and Ramsay's (1991) nonparametric kernel smoothing approach with TestGraf. Results show that half of the items display at least moderate DIF between groups and, depending on the type of analysis used, 3 to 7 items displayed large DIF. The 5 items with the most DIF were more significantly endorsed by poker players (uniform DIF) and were all related to skills, knowledge, learning, or probabilities. Poker players' interpretations of some skills-related items may lead to an overestimation of their cognitive distortions due to their total score increased by measurement artifact. Findings indicate that the current structure of the GRCS contains potential biases to be considered when poker players are surveyed. The present study conveys new and important information on bias issues to ponder carefully before using and interpreting the GRCS and other similar wide-range instruments with poker players. (PsycINFO Database Record
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".