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Three‐month Follow‐up of Brief Computerized and Therapist Interventions for Alcohol and Violence Among Teens

2009· article· en· W2039660158 on OpenAlexaff
Rebecca M. Cunningham, Maureen A. Walton, Abby L. Goldstein, Stephen T. Chermack, Jean T. Shope, C. Raymond Bingham, Marc A. Zimmerman, Frederic C. Blow

Bibliographic record

VenueAcademic Emergency Medicine · 2009
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of Toronto
FundersNational Institute on Minority Health and Health DisparitiesNational Institute on Alcohol Abuse and Alcoholism
KeywordsMedicinePsychological interventionIntervention (counseling)Brief interventionRandomized controlled trialEmergency departmentDemographicsPoison controlInjury preventionPopulationSuicide preventionFamily medicinePsychiatryDemographyMedical emergencyEnvironmental healthSurgery

Abstract

fetched live from OpenAlex

OBJECTIVES: Alcohol use and violent behaviors are well documented among adolescents and have enormous effects on morbidity and mortality. The authors hypothesized that universal computer screening of teens in an inner-city emergency department (ED), followed by a brief intervention (BI), would be 1) feasible (as measured by participation and completion of BI during the ED visit) and well received by teens (as measured by posttest process measures of intervention acceptability) and 2) effective at changing known precursors to behavior change such as attitudes, self-efficacy, and readiness to change alcohol use and violence. METHODS: Adolescent patients (ages 14-18 years) at an urban ED were approached to complete a computerized survey. The survey was conducted daily from 12 noon to 11 pm from September 2006 through November 2008. Adolescents reporting both alcohol use and violence in the past year were randomized to a control group or a 35-minute BI delivered by a computer or therapist as part of the SafERteens study. Validated measures were administered, including demographics, alcohol use, attitudes toward alcohol and violence, self-efficacy for alcohol and violence, readiness to change alcohol and violence, and process questions, including likeability of intervention. RESULTS: A total of 2,423 adolescents were screened. Thirteen percent of those approached refused. The population was 45% male, 58% African American, and 6.2% Hispanic. Of those screened, 637 adolescents (26%) screened positive; 533 were randomized to participate, and 515 completed the BI prior to discharge. The BIs were well received by the adolescents overall; 97% of those randomized to a BI self-reported that they found one intervention section "very helpful." At posttest, significant reductions in positive attitudes for alcohol use and violence and significant increases in self-efficacy related to alcohol/violence were found for both therapist and computer interventions. At 3-month follow-up there was 81% retention, and generalized estimating equations (GEE) analysis showed that participants in both interventions had significant reductions in positive attitudes for alcohol use (therapist p = 0.002, computer p = 0.0001) and violence (therapist p = 0.012, computer p = 0.007) and significant increases in self-efficacy related to violence (therapist p = 0.0.04, computer p = 0.002); alcohol self-efficacy improved in the therapist BI condition only (therapist p = 0.050, computer p = 0.083). Readiness to change was not significantly improved. CONCLUSIONS: This initial evaluation of the SafERteens study shows that universal computerized screening and BI for multiple risk behaviors among adolescents is feasible, well received, and effective at altering attitudes and self-efficacy. Future evaluations of the SafERteens study will evaluate the interventions' effects on behavioral change (alcohol use and violence) over the year following the ED visit.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.073
GPT teacher head0.371
Teacher spread0.297 · 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
GenreEmpirical

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

Citations96
Published2009
Admission routes1
Has abstractyes

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