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Record W4416202345 · doi:10.2196/75628

Solution-Focused Brief Intervention for Substance Use: Protocol for a Multisite Randomized Controlled Trial

2025· article· en· W4416202345 on OpenAlexvenueno aff
Karla González Suitt, Álvaro Vergés, Rodrigo Portilla Huidobro, Bárbara Donoso Vargas

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsRandomized controlled trialProtocol (science)Intervention (counseling)Brief interventionIntention-to-treat analysisMotivational interviewing

Abstract

fetched live from OpenAlex

BACKGROUND: Substance use is a high-impact biopsychosocial problem in Chile, where 21% of adults have experienced a severe alcohol use episode. In the past year, the prevalence rates were 12.5% for marijuana, 0.8% for cocaine, and 0.4% for cocaine base paste. Cannabis prevalence in Chile is higher than the global (4.2%) and South American (3.58%) averages. Cocaine prevalence in Chile is lower than in South America (1.62%) but higher than the global average (0.42%). No international reports are available for cocaine base paste. Mental health and substance use programs in Chilean primary care involve psychologists and social workers. Solution-focused brief interventions (SFBIs) are based on solution-focused brief therapy, a strengths-based and person-centered approach in which practitioners adopt a stance of "not being the expert," respecting clients' needs and perspectives. OBJECTIVE: This study aims to determine whether the SFBI implemented by psychosocial teams (psychologists and social workers) for individuals with alcohol and other drug use in primary health care centers leads to better outcomes than usual care. METHODS: We will conduct a randomized controlled clinical trial (ClinicalTrials.gov registration pending) comparing a 3-session SFBI (experimental group) with a single session of brief counselling as usual care (control group) in primary care. Interventions will be delivered in person by a psychologist or social worker. A total of 320 participants are expected to be recruited during preventive routine checkups using the Alcohol, Smoking, and Substance Involvement Screening Test. Participants reporting intermediate- to high-risk substance use on this screening tool will be randomly assigned to each group. Research assistants will administer instruments at baseline and at 3-, 6-, and 9-month follow-ups and will be blinded to the assigned treatments. The primary outcome assessed will be substance use patterns, while secondary outcomes include background information, depressive symptoms, anxiety symptoms, and motivation for treatment. Statistical analyses, including t tests, ANOVA, and Fisher exact tests will be conducted depending on variable type and normality. A qualitative component to assess acceptability and pertinence will include focus groups with participants and practitioners, followed by a content analysis. RESULTS: Funding for this study started in April 2024. As of the submission date of this protocol, 55 practitioners from 9 primary health care centers have been trained in SFBIs. Recruitment began in February 2025, with 73 participants enrolled and 23 who dropped out. Recruitment will continue until December 2026. No analyses have been conducted to date. Findings are expected to be published during the second half of 2028. CONCLUSIONS: This study strengthens primary care by integrating targeted psychosocial interventions for substance use into existing programs, thereby enhancing real-world applicability. If effective, the intervention could be adopted into routine care and inform public policy on mental health and substance use. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/75628.

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)0.030
metaresearch head score (Gemma)0.027
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.110
Threshold uncertainty score0.367

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.027
Meta-epidemiology (narrow)0.0080.004
Meta-epidemiology (broad)0.0150.007
Bibliometrics0.0030.004
Science and technology studies0.0050.004
Scholarly communication0.0050.004
Open science0.0040.003
Research integrity0.0070.011
Insufficient payload (model declined to judge)0.1100.015

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 head0.221
GPT teacher head0.554
Teacher spread0.332 · 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
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

Citations0
Published2025
Admission routes1
Has abstractyes

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