Substance Use and Dual Diagnosis Disorders: Future Epidemiology, Determinants, and Policies
Notice bibliographique
Résumé
Dual diagnosis or dual disorders are terms used to define the presence of an addictive disorder and another mental health disorder in an individual. With a high prevalence of dual diagnosis (above 50%) being well documented in clinical and epidemiological studies, clinicians, researchers, and policy makers have increasingly been paying attention to the challenges of identifying and implementing adequate management of cooccurring disorders/dual pathology, especially since they have been frequently associated with relapses, poor treatment engagement, and overall unsatisfactory treatment outcomes. The papers selected for this special issue represent a good panel for addressing this challenge. It is however quite certain that the subject is extremely vast and, hence, the selected topic and the papers are not an exhaustive representation of this area of dual diagnostic disorders. Nonetheless, they represent the rich and complex web of interactions which underplay dual diagnosis, which we have the pleasure of sharing with the readers. The special issue contains five papers, from different geographical regions of the world, each dealing with a different subject within this vast area. A paper from Germany titled “Reflections on Addiction in Students Using Stimulants for Neuroenhancement: A Preliminary Interview Study” uses face to face interviews with university students to explore determinants of nonmedicinal uses of methylphenidate and amphetamines for pharmacological neuroenhancement. While highlighting the need for long term empirical research, on the basis of some quite interesting results, the authors conclude that the beliefs and behavior of their sample population appear to be risky in terms of development of addiction. Such findings can help in understanding the underpinnings of addictions in student population and may have the potential to ultimately improve targeted interventions for this important group. In a paper from Malaysia titled “The Effect of Nicotine Dependence on Psychopathology in Patients with Schizophrenia,” A. Yee et al. study the prevalence of nicotine dependence in a cross-sectional study and investigate the effects of nicotine dependence on psychopathology among 180 outpatients with schizophrenia at a general hospital in Malaysia. They have observed a higher prevalence of nicotine dependence among patients with schizophrenia when compared to the general population in Malaysia. They have also observed a significant association between negative symptoms of schizophrenia and nicotine use, which supported the notion of self-medication hypothesis of schizophrenia. In another paper from Brazil titled “Revictimization of Violence Suffered by Those Diagnosed with Alcohol Dependence in the General Population,” F. G. Moreira et al. studied the vicious association of violence and alcohol dependence syndrome in a general population. Although they observed that urban and familial violence in the general population and alcohol dependence had a complex interplay, they felt that a policy of reducing familial and domestic violence may be necessary to reduce alcohol dependence. This is an interesting conclusion and calls for further validation in well-designed longitudinal studies. In a paper from Australia titled “Khat Use: What Is the Problem and What Can Be Done?” which was unique in being a qualitative research study as well as studying a minority population, Y. S. Omar et al. used the techniques of focused group discussions and thematic analysis to address khat use and evolve strategies to deal with this rapidly progressing problem. In a paper from Canada, titled “Attention Deficit Hyperactivity Disorder Symptoms, Comorbidities, Substance Use, and Social Outcomes among Men and Women in a Canadian Sample,” E. Vingilis et al. screened for ADHD symptoms and its correlates including substance use. They observed a higher lifetime cocaine use and comorbid anxiety and depression, which points to the difficulties of being able to manage this complex group of patients.
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 | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| 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) | 0,000 | 0,000 |
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; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».