PROTOCOL: Functional Family Therapy (FFT) for young people in treatment for non‐opioid drug abuse
Bibliographic record
Abstract
BACKGROUND Description of the conditionYouth drug abuse 1 While cannabis, amphetamine, cocaine and other non-opioid drugs remain illegal in most countries, surveys indicate widespread prevalence.In the US, 25.5 percent of 12 th -grade students report having used an illicit drug (any kind) within the last month (Johnston, O'Malley, Miech, Bachman & Schulenberg, 2014).In Canada, 21 percent of 15-24 year olds report having used of some kind of illicit drugs within the last year (Health Canada, 2011).In Australia, seven percent 12-17 year olds report using some kind of drug within the last month (White & Smith, 2009).The European Monitoring Centre for Drugs and Drug Addiction has found that that within Europe prevalence differs significantly from country to country but that overall around a quarter of Europeans report having used some kind of illicit drug in their lifetime (European Monitoring Centre for Drugs and Drug Addiction (EMCDDA), 2013).of the kind that persists beyond the experimentation phase is a severe problem worldwide (United Nations Office on Drugs and Crime (UNODC), 2010).Abuse of non-opioid drugs such as cannabis, amphetamine and cocaine is strongly associated with a broad range of negative health implications such as traffic accidents, sexually transmitted diseases, mental problems and suicide as well as social problems including poor academic achievement, delinquency and violent behavior (Deas & Thomas, 2001;Essau, 2006;Rowe & Liddle, 2006; ONDCP, 2000;Shelton, Taylor, Bonner & van den Bree, 2009;Nordstrom & Levin, 2007;Lynskey & Hall, 2000).The prevalence of specific kinds of illicit drug abuse varies significantly, with cannabis generally being the most commonly used drug.In the US, 22.7 percent of 12 th -grade students report having used marijuana/hashish (types of cannabis), 4.1 percent amphetamine, and 1.1 percent cocaine during the last 30 days before the National Survey on Drug Use conducted in 2013 (Johnston et al., 2014).The European Drug Report of 2013 indicates that 11.7 percent of the 15 to 34 year-olds in Europe have used cannabis, 1.3 percent amphetamine, and 1.9 percent used cocaine during the last year (EMCDDA, 2013).Although not all young drug users progress to severe dependence, some do and may therefore require treatment (see e.g.Crowley, Macdonald, Whitmore & Mikulich, 1998).Research draws attention to the significant gap between the number of young people classified as in need of treatment and the number of young people who actually receive such treatment (SAMHSA, 2010; National Survey on Drug Use and Health (NSDUH), 2007).In the US, for example, 7.2 million people aged 12 or older are classified as needing treatment 1 The terms 'use', 'abuse' and 'dependence' are often used interchangeably and refer to an addiction stage of non-medical drug usage.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.339 | 0.055 |
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".