The relationship between gambling fallacies and problem gambling.
Bibliographic record
Abstract
The cognitive model of problem gambling posits that erroneous gambling-related fallacies are key in the development and maintenance of problem gambling. However, this contention is based on cross-sectional rather than longitudinal associations between these constructs, and gambling fallacy instruments that may have inflated this associated by their inclusion of problem gambling symptomatology. The current research re-evaluates the relationship between problem gambling and gambling-specific erroneous cognitions in a 5-year longitudinal study of gambling using a psychometrically sound measure of erroneous gambling-related cognitions. The sample used in this study (n = 4,121) was recruited from the general population in Ontario, Canada, and the retention rate over 5 years was exceptionally high (93.9%). The total sample was similar, in age and gender distributions, to the census data at the time of data collection for Canadian adults (18-24 years, n = 265, 55.8% female; 25-44 years, n = 1,667, 56.4% female; 45-64 years, n = 1,731, 55.4% female; 65 + years, n = 458, 44.75% female). Results of both cross-sectional and longitudinal analyses confirm that gambling-specific fallacies appear to be etiologically related to the subsequent appearance of problem gambling, but to a weaker degree than previously presumed, and in a bidirectional manner. (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 | 0.001 | 0.019 |
| 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 | 0.001 |
| 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 | 0.000 |
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".