Notice bibliographique
Résumé
Marijuana is a preparation from the Cannabis plant that is smoked. The active ingredient in Cannabis is delta-9-tetrahydrocannabinol (THC). Hashish, charas, ganja, sinsemilla, bhang, and hash oil are some forms in which Cannabis is used. Marijuana or Cannabis use is becoming a major public health problem. According to Youth Risk Behavior Survey (YRBS) done in 2007 the percentage of students that used marijuana one or more times during their lives was 38.1% (95% CI: 35.5-40.7) and those that used marijuana in the past 30 days was 19.7% (95% CI: 17.8-21.8) (National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, 2008). In a prevalence study of adults in 2001-02 in the United States it was found that 4.1% of the adults were marijuana users in the past year (Compton, Grant, Colliver, Glantz, & Stinson, 2004). Diagnostic and Statistical Manual-IV-TR (American Psychiatric Association, 2000) has defined marijuana abuse as repeated instances of use under hazardous conditions; repeated, clinically meaningful impairment in social/occupational/educational functioning; or legal problems related to marijuana use; and marijuana dependence as increased tolerance, compulsive use, impaired control, and continued use despite physical and psychological problems caused or exacerbated by use. Compton and colleagues (2004) found that marijuana abuse increased from 0.9% in 1991-92 to 1.1% in 2001-02, and marijuana dependence increased from 0.3% to 0.4%. In absolute numbers this translates to an increase of 0.8 million people who are marijuana abusers or dependent on it thereby implying a significant public health problem. Several studies have been done to understand the determinants of marijuana use. A study by Tu, Rather, and Johnson (2008) examined gender differences with regard to marijuana use in a Canadian sample of adolescents. The study found that 47% of the sample had used marijuana. The characteristics of male heavy users were being in Grade 9 or higher, being Aboriginal, reporting poorer socio-economic status, never feeling like an outsider, frequently using alcohol and tobacco, and having lower satisfaction with family, friends, and school. The characteristics of female heavy users were being in higher grades, reporting poorer socio-economic status, having poorer mental health, having poorer academic performance, frequently using alcohol and tobacco, and having lower satisfaction with their school. An Australian study by Hayatbakhsh and colleagues (2008) examined early childhood factors for initiation of tobacco, alcohol, and marijuana and found that disrupted families, drug-using parents, childhood problem behaviors, and poor parental monitoring and supervision in childhood were associated with early initiation. An American study by Brook, Saar, and Brook (2008) showed that exposure to violent television programs in late adolescence was related to marijuana use. A study done with Belgian teenagers found that Cannabis use was higher among general education students and truants (Kohn, Dramaix, Favresse, Kittel, & Piette, 2005). Another study by Kohn and colleagues (2004) found that age, amount of income in the family, strong peer group integration and tobacco use were associated with Cannabis experimentation; while age, gender, nationality, average family integration and other drugs use such as addiction to nicotine were related to the current consumption of marijuana. A study by Crano, Siegel, Alvaro, Lac, and Hemovich (2008) looked at the differences between resolute non users of marijuana, vulnerable non users and users. The three groups differed with regard to religiosity, delinquency (self and friends'), refusal strength, sensation seeking, parental monitoring and warmth, and adult supervision. In a study of maternal Cannabis use during early pregnancy it was found that the strongest determinant was Cannabis use by the biological father of the child followed by being single or having a partner without being married, childhood trauma and delinquency (El Marroun et al. …
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), 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 ».