Validation of a Sports Betting Adaptation to the Problem Gambling Severity Index in Young Adults
Bibliographic record
Abstract
Background. Sports betting is a rapidly growing addictive behavior, especially among young adults. As such, there is a need for measuring problem sports betting behaviors and consequences separately from established generalized gambling measures. The present study provides support for a sports betting adaptation of the Problem Gambling Severity Index (PGSI-SB). Methods. We recruited a sample (N=221) of young adults aged 18-29 (Mage=24.4; 22% female; 13.2% Hispanic; 68.6% college degree) from 36 different US states. Eligibility criteria included ≥2 sports betting days in the past month. Results. Confirmatory factor analyses showed support for both a single and two-factor model with subscales for problematic behavior (e.g., dependence) and negative consequences. The PGSI-SB was strongly correlated with the original PGSI in terms of scale-level and item-level correlations (i.e., convergent validity). Aim 3 established predictive validity of the single-factor PGSI-SB via significant associations with three indices of past two-week sports betting: frequency, number of bets, and total amount wagered. Predictive validity for the two-factor model was impacted by multicollinearity, given high correlation between subscales. Conclusions. Findings establish the merits of a dedicated problem sports betting measure for young adults, which is a key step towards enhancing the quality and consistency of sports betting research.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".