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Brief Motivational Interviewing for DWI Recidivists Who Abuse Alcohol and Are Not Participating in DWI Intervention: A Randomized Controlled Trial

2009· article· en· W2126502904 on OpenAlexafffund
Thomas G. Brown, M Dongier, Marie Claude Ouimet, Jacques Tremblay, Florence Chanut, Lucie Legault, N.M.K. Ng Ying Kin

Bibliographic record

VenueAlcoholism Clinical and Experimental Research · 2009
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsInstitut national de psychiatrie légale Philippe-PinelMcGill UniversityDouglas Mental Health University InstituteUniversité de MontréalUniversité de Sherbrooke
FundersCanadian Psychiatric Research Foundation
KeywordsMotivational interviewingPsychological interventionRandomized controlled trialAlcohol abusePoison controlBrief interventionIntervention (counseling)PsychiatryInjury preventionMedicineSuicide preventionClinical psychologyPsychologyBinge drinkingEnvironmental healthSurgery

Abstract

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BACKGROUND: Driving while impaired (DWI) recidivists with unresolved alcohol use problems pose an ongoing risk for traffic safety. Following conviction, many do not participate in mandated alcohol evaluation and intervention programs, or continue to drink problematically after being relicensed. This study investigated if, in DWI recidivists with alcohol problems and not currently involved in DWI intervention, Brief Motivational Interviewing (BMI) produced greater reductions in risky drinking at 6- and 12-month follow-up compared to an information-advice control condition. Additional analyses explored whether BMI was associated with greater readiness to change, subsequent substance abuse treatment service utilization, and satisfaction compared to the control condition. METHODS: Male and female recidivists with drinking problems and not currently engaged in DWI intervention were recruited, evaluated, and then randomly assigned to receive 1 of 2 manualized interventions: 30-minute BMI session or information-advice. Participants, interviewers, researchers, and statisticians were blind to assignment. Outcomes were changed in: percent of risky drinking days (i.e., > or =3 standard drinks/d for males; > or =2 for females) in the previous 6 months derived from the Timeline Followback, biomarkers of alcohol abuse (GGT, AST, ALT, MCV) by blood assay, and alcohol abuse-related behaviors using the MMPI-Mac scale. Data from the Readiness to Change Questionnaire, a substance abuse service utilization questionnaire, and the Client Satisfaction Scale were also collected. RESULTS: Analyses revealed significant declines in risky drinking with both interventions. BMI (n = 92) resulted in a 25% reduction in risky drinking days at 12-month follow-up, which compared to the control intervention (n = 92) represented a significant decline from 6-month levels. Exposure to BMI also produced significantly greater improvement at 6-month follow-up in a biomarker of alcohol abuse and a behavioral measure related to recidivism risk. Exploration of readiness to change, substance abuse service utilization, and satisfaction with intervention indicated a perception of BMI being more useful in coping with problems. CONCLUSIONS: Brief MI approaches warrant further implementation and effectiveness research as an opportunistic DWI intervention strategy to reduce risks associated with alcohol use outside of clinical and DWI relicensing settings.

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)0.002
metaresearch head score (Gemma)0.004
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.270
GPT teacher head0.521
Teacher spread0.251 · 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

Citations63
Published2009
Admission routes2
Has abstractyes

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