9. A critical review of adolescent problem gambling assessment instruments
Bibliographic record
Abstract
The field of youth gambling assessment is in its infancy. Currently four youth problem gambling instruments have been used to identify adolescent problem gamblers: a) South Oaks Gambling Screen-Revised for Adolescents (SOGS-RA); b) DSM-IV-Juvenile (DSM-IV-J) and the related DSM-IV-Multiple Response-Juvenile (DSM-IV-MR-J); c) Massachusetts Gambling Screen (MAGS) and d) Canadian Adolescent Gambling Inventory (CAGI). Three of the four instruments are adaptations of adult instruments, and none of the four have undergone rigorous psychometric evaluation. While these instruments are used with varying populations in divergent settings, the psychometric properties for their use in these populations and settings are unknown. This review provides information about the instruments and makes suggestions for further instrument development and refinement. Each instrument is described in terms of its development, content, intended purpose, psychometric properties, administration method, scoring instructions, and interpretation. Strengths and limitations of each instrument are compared for both research and clinical purposes. Existing instruments are used to make clinical, scientific, and public policy decisions, and therefore, it is critical that these instruments demonstrate evidence of reliability, validity and accuracy. It is recommended that the field adopt testing standards for the development and use of adolescent problem gambling scales, and generate a body of rigorous psychometric research that demonstrates reliability, validity, and classification accuracy. Ultimately, the goal is to improve measurement precision in identifying youth problem gamblers.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".