Development and validation of the Gambling Pathways Questionnaire (GPQ).
Bibliographic record
Abstract
The Pathways Model (Blaszczynski & Nower, 2002) is a theoretical framework that proposes three pathways for identifying etiological subtypes of problem gamblers. The model has been used to assist clinicians in developing individualized treatments that target not only the gambling behavior but also associated risk factors that may undermine recovery and precipitate relapse. The current study sought to develop and validate a new screening instrument, based on the Pathways Model for treatment-seeking gamblers. Participants were gamblers age 18 and over who scored 1+ symptoms on the Problem Gambling Severity Index of the Canadian Problem Gambling Index and presented to one of 22 participating treatment centers in Canada, the United States, and Australia (N = 1,176). Data were collected on 127 items, consisting of 62 core items that reflected variables in the Pathways Model and 65 experimental items derived from recent scholarly literature in gambling etiology. Exploratory and confirmatory factor analyses identified the following six factors: Antisocial Impulsive Risk-Taking, Stress-Coping, Mood Pre-Problem-Gambling Onset, Mood Post-Problem-Gambling Onset, Child Maltreatment, and Meaning Motivation. The Gambling Pathways Questionnaire showed excellent internal consistency (α = .937), with good to high reliability found for each of the six factors, ranging from .851 to .945. Cluster analysis results demonstrated that the three-factor model produced good model fit to the data: Cluster 1 (Behaviorally Conditioned Subtype), Cluster 2 (Emotionally Vulnerable Subtype) and Cluster 3 (Antisocial, Impulsive Risk-Taking Subtype). The present study is the first to present an empirical measure for assigning problem gamblers to etiological subtypes for use as a screening tool in treatment settings. (PsycINFO Database Record
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".