Centre for Addiction and Mental Health Inventory of Gambling Situations: Evaluation of the Factor Structure, Reliability, and External Correlations
Bibliographic record
Abstract
The development and evaluation of the Centre for Addiction and Mental Health Inventory of Gambling Situations (CAMH-IGS) is described. The CAMH-IGS is based on a cognitive-behavioural approach to addiction that sees excessive gambling as a pattern of behaviour which is learned, and which can be changed. The CAMH-IGS is designed to determine the patterns of behaviour, thoughts or feelings which may trigger problematic gambling, with the goal of developing tailored treatment and relapse-prevention approaches for clients. The information can be used in treatment planning. A sample of 524 gamblers that included 323 problem and probable pathological gamblers was used to evaluate the factor structure, reliability, and external correlations of the CAMH-IGS. The results show that the CAMH-IGS consists of 10 internally reliable subscales that can identify individual differences between clients.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".