P02-27 - Pathological Gambling in General Population: Preliminary Study
Bibliographic record
Abstract
Pathological gambling is characterized in DSM IV-TR as one of the disorders of impulse control. Problem gambling is also part of what is considered behavioural addictions with intrusive thoughts about the game, are spending more and more important to play etc. Objectives There is no epidemiological study in France, that's why we make an epidemiological study on the prevalence of pathological gambling. Methods We wanted to study the prevalence of pathological gambling in a sample of 529 persons: 368 gamers of Pari Mutuel Urbain and La Française des Jeux, and 161 persons in the general population. We used as instruments: SOGS for screening of pathological gambling, BIS-10 for impulsiveness's evaluation, HAD scale to assess anxiety and depression and ASRS for the evaluation of attention deficit disorder / hyperactivity disorder. Results The results show that the rate of pathological gambling in general population is 1.24% (this result is similar to those found in other countries such as Quebec) Men are overrepresented in the group of pathological gamblers (88.9%), also with consumption of alcohol and tobacco. Depression and anxiety are particularly high, 40% of JPs with an anxiety score significantly higher. Conclusions It would be necessary to establish follow-up studies of populations and patients as well as specific studies on people who frequent casinos, racetracks and Internet gambling. Almost 20% of players have a gambling problem or risk and these people do not consult despite their psychological problems, family, work, debts…
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".