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
Bibliographic record
Abstract
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.
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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 | 0.059 | 0.075 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.019 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.053 | 0.005 |
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.
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".