ANTISOCIAL PERSONALITY DISORDER AND GAMBLING: COMMENTS ON PIETRZAK & PETRY (2005)
Notice bibliographique
Résumé
I was pleased to read the article entitled ‘Antisocial personality disorder is associated with increased severity of gambling, medical, drug and psychiatric problems among treatment-seeking pathological gamblers’ by Robert H. Pietrzak & Nancy M. Petry in Addiction[1]. In the study, the authors used a relatively larger sample of treatment-seeking pathological gamblers not only to replicate prior published studies but also to explore other problems associated with pathological gambling such as medical and drug problems, etc. which are influenced by antisocial personality disorder (ASPD). However, the article left this reader with some questions. First, the prevalence of pathological gambling with ASPD reported by the authors (16.5%) is much lower than that observed in other community-based studies, including the St Louis ECA study (35%) [2] and the Edmonton Epidemiologic Catchment Area study in Canada (40%) [3]. The differences might be due to using different diagnostic criteria for ASPD [from Diagnostic and Statistical Manual version III (DSM-III) to DSM-IV], by increased accessibility to gambling or by different study parameters. Substance abuse and depression are common comorbidities for pathological gamblers. Many people with ASPD have problems of substance abuse or depression as well. It is not clear from the study's findings that the increased severity of pathological gambling was truly caused by ASPD or by substance abuse or depression. Secondly, all the participants were treatment seekers and screened first by telephone interview. Most probably, the findings are not generalizable to non-treatment-seeking pathological gamblers with or without ASPD due to sample selection bias. Also, as the sample was selected from Connecticut, the comparison of pathological gamblers with or without ASPD in other states or countries with varying access to gambling facilities may present different result patterns. Thirdly, the logistic regression analysis on the predictors of ASPD gave inconsistent results in terms of whether or not age and education are predictors for ASPD. The first step of analysis of demographic variables alone showed significant results on age and education as predictors for ASPD. But in the second step, which included gambling, drug and medical variables, the age and education were no longer independent predictors for ASPD in pathological gamblers. The authors did not make it clear whether or not age or education is a predictor of ASPD for pathological gamblers. Previous studies on a twin sample have shown that age and education are not predictors for ASPD [4]. In conclusion, the study employed a relatively larger sample and some of the results are consistent with previous research findings on pathological gambling with ASPD. However, the sample selection bias, and the much lower prevalence of ASPD in pathological gamblers compared with that from the general population-based studies, make the validity and generalizability of the study questionable. More research with randomized large samples is needed in order to extend this study to general populations.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».