Trastornos de personalidad y juego patológico en adolescentes y jóvenes con dependencia de las máquinas tragamonedas
Bibliographic record
Abstract
The object of this study was to explore the relationship between patterns of maladaptive personality and pathological gambling in a group of gamblers addicted to slot machines. The sample consisted of 120 male and female subjects divided into two groups: a group of 60 pathological gamblers aged 18 to 24 who attended the Pathological Gambling Recovery Program at the Instituto de Psicología Integral del Perú (IPIP), and a group of 60 non-gamblers aged 18 to 24 who were psychology students of a private university in Lima. Data was obtained using the Millon Clinical Multiaxial Inventory II (MCMI-II), the Pathological Gambling Brief Questionnaire (PGBQ), and the Structured Clinical Interview for Pathological Gambling (SCI-PG). The results showed that 81,7 % of the pathological gamblers addicted to slot machines had at least one personality disorder, in comparison with the other group which yielded 40%. The most prevalent personality disorder was the self-defeating disorder, followed by the passive-aggressive, narcissistic and borderline disorders. Moreover, there are significant differences in maladaptive personality patterns between gamblers and non-gamblers according to Millon’s theory.
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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 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".