Bibliographic record
Abstract
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. …
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".