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Record W2892548388 · doi:10.1186/s13011-018-0171-4

Universal cannabis outcomes from the Climate and Preventure (CAP) study: a cluster randomised controlled trial

2018· article· en· W2892548388 on OpenAlexaff
Nicola C. Newton, Maree Teesson, Marius Mather, Katrina E. Champion, Emma Barrett, Lexine Stapinski, Natacha Carragher, Erin Kelly, Patricia Conrod, Tim Slade

Bibliographic record

VenueSubstance Abuse Treatment Prevention and Policy · 2018
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersNational Health and Medical Research CouncilNational Medical Research CouncilMedical Research Council
KeywordsCannabisMedicineCluster randomised controlled trialRandomized controlled trialEnvironmental healthDemographyPsychiatrySurgery

Abstract

fetched live from OpenAlex

BACKGROUND: The Climate and Preventure (CAP) study was the first trial to assess and demonstrate the effectiveness of a combined universal and selective approach for preventing alcohol use and related harms among adolescents. The current paper reports universal effects from the CAP study on cannabis-related outcomes over three years. METHODS: A cluster randomized controlled trial was conducted with 2190 students from twenty-six Australian high schools (mean age: 13.3 yrs., SD 0.48). Participants were randomised to one of four conditions; universal prevention for all students (Climate); selective prevention for high-risk students (Preventure); combined universal and selective prevention (Climate and Preventure; CAP); or health education as usual (Control). Participants were assessed at baseline, post intervention (6-9 months post baseline), and at 12-, 24- and 36-months, on measures of cannabis use, knowledge and related harms. This paper compares cannabis-related knowledge, harms and cannabis use in the Control, Climate and CAP groups as specified in the protocol, using multilevel mixed linear models to assess outcomes. RESULTS: Compared to Control, the Climate and CAP groups showed significantly greater increases in cannabis-related knowledge initially (p < 0.001), and had higher knowledge at the 6, 12 and 24-month follow-ups. There was no significant difference between the Climate and CAP groups. While no differences were detected between Control and the CAP and Climate groups on cannabis use or cannabis-related harms, the prevalence of these outcomes was lower than anticipated, possibly limiting power to detect intervention effects. Additional Bayesian analyses exploring confidence in accepting the null hypothesis showed there was insufficient evidence to conclude that the interventions had no effect, or to conclude that they had a meaningfully large effect. CONCLUSIONS: Both the universal Climate and the combined CAP programs were effective in increasing cannabis-related knowledge for up to 2 years. The evidence was inconclusive regarding whether the interventions reduced cannabis use and cannabis-related harms. A longer-term follow-up will ascertain whether the interventions become effective in reducing these outcomes as adolescents transition into early adulthood. TRIAL REGISTRATION: This trial was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12612000026820) on the 6th of January 2012, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=347906&isReview=true.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.022
GPT teacher head0.315
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations30
Published2018
Admission routes1
Has abstractyes

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