A Spanish Validation of the Canadian Adolescent Gambling Inventory (CAGI)
Bibliographic record
Abstract
Aims. Large-scale epidemiological studies show a significant prevalence of gambling disorder (GD) during adolescence and emerging adulthood, and highlight the need to identify gambling-related behaviors at early ages. However, there are only a handful of screening instruments for this population and many studies measuring youth gambling problems use adult instruments that may not be developmentally appropriate. The aim of this study was to validate a Spanish version of the Canadian Adolescent Gambling Inventory (CAGI) among late adolescent and young adults and to explore its psychometric properties. Methods. The sample (16-29 years-old) included a clinical group (n=55) with gambling disorder (GD) patients and a control group (n=340). Results. Exploratory factor analysis yielded one factor as the best model. This 24-item scale demonstrated satisfactory reliability (internal consistency, Cronbach’s alpha, =0.91), satisfactory convergent validity as measured by correlation with SOGS (r=.74), and excellent classification accuracy (AUC=0.99; sensitivity=.98; and specificity=.99). Conclusions. Our results provide empirical support for our validation of the Spanish version of the CAGI. We uphold that the Spanish CAGI can be used as a brief, reliable and valid instrument to assess gambling problems in Spanish youth.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".