Validation of the problem gambling severity index using confirmatory factor analysis and rasch modelling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Problem Gambling Severity Index (PGSI), a screening tool used to measure the severity of gambling problems in general population research, was subjected to confirmatory factor analysis and Rasch modelling to (a) confirm the one-factor structure; (b) assess how well the items measure the continuum of problem gambling severity; (c) identify sources of differential item functioning among relevant subpopulations of gamblers. Analyses were conducted on a nationally representative sample of over 25,000 gamblers compiled by merging data from the Canadian Community Health Survey and Canadian Problem Gambling Index (CPGI) integrated datasets. Results provided support for a one-factor model that was invariant across gender, age, income level, and gambler type. Rasch modelling revealed a well-fitting, unidimensional model with no miss-fitting items. The average severity assessed by the PGSI is consistent with moderately severe problem gambling. The PGSI is therefore weak in assessing low to moderate problem severity, a notable limitation of most brief gambling screens. Evidence of clinically significant differential item functioning was found with only one item, borrowing money to gamble, which behaved differently in gamblers who play electronic gaming machines or casino games compared to gamblers who avoid these games.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it