Self-generated motives for gambling in two population-based samples of gamblers
Bibliographic record
Abstract
In the present study, self-generated responses to a question regarding reasons for gambling from two epidemiological surveys were combined and placed into another earlier motivational model for alcohol use, adapted for gambling. Of the 3601 reasons, 954 could be categorised into the model's categories: (a) coping motives (internal, negative reinforcement); (b) enhancement motives (internal, positive reinforcement); and (c) social motives (external, positive reinforcement). Results indicate that coping gamblers experienced greater gambling severity and psychopathology, enhancement gamblers were most likely to gamble while intoxicated and social gamblers were more likely to choose socially-related gambling. An examination of remaining motives suggests additional categories may be warranted -- specifically financial and charitable reasons. These findings offer some support for the model; however, it may need to be expanded to account for other motives. The study highlights the advantages and limitations of using self-generated reasons to study gambling motivation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".