Pathways to Pathological Gambling: Component Analysis of Variables Related to Pathological Gambling
Bibliographic record
Abstract
This study used principal components analysis to examine the structure of variables associated with pathological gambling. A large battery of questionnaires was administered to a sample of 141 gamblers who ranged from non-problem gamblers to severe pathological gamblers. We found a significant relationship between severity of pathological gambling and various measures of impulsivity, depression, anxiety, erroneous beliefs, and reports of early wins. Component analysis of these variables found four distinct components: emotional vulnerability, impulsivity, erroneous beliefs, and the experiences of wins. Component scores based on these components were regressed onto pathological gambling. Emotional vulnerability had the largest effect (β = 0.54), followed by early wins (β = 0.32), erroneous beliefs (β = 0.31), and impulsivity (β = 0.23). The overall model accounted for 53.4% of the variance of pathological gambling. The findings confirm the idea that there may be several different risk factors that explain the development of pathological gambling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".