Electronic-Screening, Brief Intervention and Referral to Treatment (e-SBIRT) for Addictive Disorders: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Addictive disorders are significant global public health burdens. Treatment uptake with these disorders is low and outcomes can be mixed. Electronic screening, brief intervention, and referral to treatment (e-SBIRT) programs have potential to improve uptake and treatment outcomes. To date, however, no prior review of the literature has been conducted to gauge the effectiveness of e-SBIRT for addictive disorders. METHODS: We conducted a systematic review and meta-analysis of the literature concerning e-SBIRT for addictive disorders by surveying the MEDLINE, PubMed, Web of Science, Scopus, Embase, and PsycInfo databases on January 17, 2023. RESULTS: Ten articles were included at analysis reporting evaluation of e-SBIRT interventions for substance use disorders including alcohol use in a variety of settings. No articles were identified regarding treatment for behavioral addictions such as disordered/harmful gambling. Meta-analysis found e-SBIRT to be effective at reducing drinking frequency in the short term only. e-SBIRT was not found to be advantageous over control conditions for abstinence or other treatment outcomes. We identified and described common components of e-SBIRT programs and assessed the quality of available evidence, which was generally poor. CONCLUSION: The present findings suggest that research regarding e-SBIRT is concentrated exclusively on higher-risk substance use. There is a lack of consensus regarding the effectiveness of e-SBIRT for addictive disorders. Although common features exist, e-SBIRT designs are variable, which complicates identification of the most effective components. Overall, the quality of outcome evidence is low, and furthermore, high-quality experimental treatment evaluation research is needed.
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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 | 0.019 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".