Etiology of problem gambling in Canada.
Bibliographic record
Abstract
OBJECTIVE: To conduct a large-scale national cohort study to identify the current etiological risk factors for problem gambling in Canada. METHOD: A cohort of 10,119 Canadian gamblers completed a comprehensive self-administered online questionnaire in 2018 and were reassessed in 2019. At baseline, the sample contained 1,388 at-risk gamblers, 1,346 problem gamblers, and 2,710 with a major DSM-5 mental health disorder. A total of 108 independent variables (IVs) were available for analysis, as well as the self-report of perceived causes of gambling-related problems for 1,261 individuals. RESULTS: The strongest multivariate predictors of current and future problem gambling were "gambling-related" variables (i.e., current and past problem gambling, intensive gambling involvement, playing electronic gambling machines (EGMs), gambling fallacies, socializing with other people having gambling-related problems, and family history of having gambling-related problems). Beyond gambling-related variables, greater impulsivity and lower household income were robustly predictive. Thirteen additional variables were either concurrently or prospectively predictive, but not both. In contrast to the many different quantitative predictors, self-reported causes tended to be singular and psychologically oriented (i.e., desire to win money, boredom, stress, poor self-control). CONCLUSIONS: The predictors of problematic gambling in the present study are very similar to the predictors identified in prior international longitudinal and cross-sectional research. This implies core cross-cultural risk factors, with gambling-related variables and impulsivity being most important, and comorbidities and demographic variables having more modest contributions. The additional value of the present results is that they comprehensively identify the relative importance of all known etiologically relevant variables within a current Canadian context. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".