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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".