The problem gambling measure: a revision of the problem & pathological gambling measure to better predict at-risk and chronic gambling
Bibliographic record
Abstract
Most problem gambling (PG) assessment instruments classify individuals with subthreshold levels of problem gambling symptomatology as ‘at-risk’, implying a future risk of developing more serious problems. However, this convention lacks empirical support. The present study aimed to develop an empirically supported revision of the Problem and Pathological Gambling Measure (PPGM) that (1) better assesses the risk of future gambling-related harm (GRH) and PG as well as (2) better predicts cases in which PG persists. Data from the Alberta Gambling Research Institute’s National Project Baseline and Follow-up Online Panel Surveys (n = 4676) were used to identify predictors of future GRH and PG. Five variables maximized prediction power: PPGM total score, problem perception, rated importance of gambling as a leisure activity, largest single day gambling loss, and breadth of monthly gambling involvement. A 16-point scale was produced based on the relative risk of developing GRH and PG and Receiver Operating Characteristic (ROC) analyses found that scores of 1, 4, and 8 best captured a gradient of risk for future GRH or PG. Regarding chronicity, a total score of 7 and higher was found to be most parsimoniously predictive of chronic PG. The revised instrument was renamed the Problem Gambling Measure (PGM).
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".