ArticleJournal of substance abuse treatment2019
Exploring the components of an efficacious computer brief intervention for reducing marijuana use among adults in the emergency department.
Article in Journal of substance abuse treatment, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- Review
- Technology-Based Interventions for Substance Use Treatment Among People Who Identify as African American or Black, Hispanic or Latino, and American Indian or Alaska Native: Scoping Review.Journal of medical Internet research · 2024Article
- Firearm safety counseling among caregivers of high-school age teens: Results from a National Survey.Preventive medicine · 2022Article
- Factors related to substance use treatment attendance after peer recovery coach intervention in the emergency department.Drug and alcohol dependence reports · 2022Article
- Optimizing scalable, technology-supported behavioral interventions to prevent opioid misuse among adolescents and young adults in the emergency department: A randomized controlled trial protocol.Contemporary clinical trials · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveTo examine the efficacious components of a computer-delivered brief intervention (CBI) for reducing marijuana use among adults presenting to a low-income urban emergency department (ED), which a prior report found to decrease marijuana use at a 6-month follow-up.
methodParticipants were 237 ED patients reporting recent drug use (46% male; 54% African-American; mean age, 30.7) who were randomized to receive a CBI consisting of an interactive program guided by a virtual health counselor. The primary outcome was past 30-day marijuana use at 6-month follow-up assessed using the Timeline Follow-Back (TLFB). Intervention components related to change in marijuana use at 6 month follow-up examined in the current study included participant responses to items within five CBI domains that were rooted in motivational interviewing: goals for change, strengths, evoking-change (concerns about use and benefits of change), challenges, and tools for change.
resultsThe evoking-change domain was related to significant reductions in marijuana use at 6 months (B = -2.91, SE = 1.10, p < .01). Within this domain, items focused on concerns about family and friends were related to reductions in marijuana use of up to 5.5 fewer days of marijuana use in the past month (B = -5.49, SE = 1.63, p < .01).
conclusionsAn ED-based brief intervention, delivered by computer, was effective in reducing marijuana use. Intervention components focused on perceived concerns about use and benefits of change in relation to family and friends were critical domains within a CBI associated with reductions in marijuana use at 6-month follow-up.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.