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Record W2121966275 · doi:10.1186/2046-4053-1-22

Effectiveness of brief interventions as part of the screening, brief intervention and referral to treatment (SBIRT) model for reducing the non-medical use of psychoactive substances: a systematic review protocol

2012· review· en· W2121966275 on OpenAlexafffundabout
Matthew M. Young, Adrienne Stevens, Amy J. Porath-Waller, Tyler Pirie, Chantelle Garritty, Becky Skidmore, Lucy Turner, Cheryl Arratoon, Nancy Haley, Karen Leslie, Rhoda Reardon, Beth Sproule, Jeremy Grimshaw, David Moher

Bibliographic record

VenueSystematic Reviews · 2012
Typereview
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of OttawaCentre for Addiction and Mental HealthCollege of Physicians and Surgeons of OntarioUniversité de MontréalCentre Hospitalier Universitaire Sainte-JustineOttawa HospitalUniversity of TorontoCanadian Centre on Substance Use and Addiction
FundersUniversity of TorontoCanadian Institutes of Health ResearchDepartment of Psychiatry, University of TorontoUniversity of Ottawa
KeywordsMedicinePsychological interventionBrief interventionPsycINFOMEDLINECINAHLCochrane LibraryRandomized controlled trialFamily medicineSystematic reviewPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: There is a significant public health burden associated with substance use in Canada. The early detection and/or treatment of risky substance use has the potential to dramatically improve outcomes for those who experience harms from the non-medical use of psychoactive substances, particularly adolescents whose brains are still undergoing development. The Screening, Brief Intervention, and Referral to Treatment model is a comprehensive, integrated approach for the delivery of early intervention and treatment services for individuals experiencing substance use-related harms, as well as those who are at risk of experiencing such harm. METHODS: This article describes the protocol for a systematic review of the effectiveness of brief interventions as part of the Screening, Brief Intervention, and Referral to Treatment model for reducing the non-medical use of psychoactive substances. Studies will be selected in which brief interventions target non-medical psychoactive substance use (excluding alcohol, nicotine, or caffeine) among those 12 years and older who are opportunistically screened and deemed at risk of harms related to psychoactive substance use. We will include one-on-one verbal interventions and exclude non-verbal brief interventions (for example, the provision of information such as a pamphlet or online interventions) and group interventions. Primary, secondary and adverse outcomes of interest are prespecified. Randomized controlled trials will be included; non-randomized controlled trials, controlled before-after studies and interrupted time series designs will be considered in the absence of randomized controlled trials. We will search several bibliographic databases (for example, MEDLINE, EMBASE, CINAHL, PsycINFO, CORK) and search sources for grey literature. We will meta-analyze studies where possible. We will conduct subgroup analyses, if possible, according to drug class and intervention setting. DISCUSSION: This review will provide evidence on the effectiveness of brief interventions as part of the Screening, Brief Intervention, and Referral to Treatment protocol aimed at the non-medical use of psychoactive substances and may provide guidance as to where future research might be most beneficial.

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)0.059
metaresearch head score (Gemma)0.075
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.059
Threshold uncertainty score0.314

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0590.075
Meta-epidemiology (narrow)0.0050.004
Meta-epidemiology (broad)0.0160.019
Bibliometrics0.0110.010
Science and technology studies0.0030.003
Scholarly communication0.0060.007
Open science0.0050.004
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0530.005

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 head0.230
GPT teacher head0.463
Teacher spread0.233 · 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 designSystematic review
Domainnot available
GenreProtocol

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

Citations84
Published2012
Admission routes3
Has abstractyes

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