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Record W2781980174 · doi:10.2196/medinform.7355

Stage-Based Mobile Intervention for Substance Use Disorders in Primary Care: Development and Test of Acceptability

2018· article· en· W2781980174 on OpenAlexvenueno aff
Deborah A. Levesque, Cindy Umanzor, Emma de Aguiar

Bibliographic record

VenueJMIR Medical Informatics · 2018
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
FundersNational Institute on Drug Abuse
KeywordsTest (biology)Intervention (counseling)MedicinePrimary careSubstance useComputer scienceMedical emergencyNursingPsychiatryFamily medicine

Abstract

fetched live from OpenAlex

BACKGROUND: In 2016, 21 million Americans aged 12 years and older needed treatment for a substance use disorder (SUD). However, only 10% to 11% of individuals requiring SUD treatment received it. Given their access to patients, primary care providers are in a unique position to perform universal Screening, Brief Intervention, and Referral to Treatment (SBIRT) to identify individuals at risk, fill gaps in services, and make referrals to specialty treatment when indicated. Major barriers to SBIRT include limited time among providers and low motivation to change among many patients. OBJECTIVE: The objective of this study was to develop and test the acceptability of a prototype of a mobile-delivered substance use risk intervention (SURI) for primary care patients and a clinical dashboard for providers that can address major barriers to SBIRT for risky drug use. The SURI delivers screening and feedback on SUD risk via mobile tools to patients at home or in the waiting room; for patients at risk, it also delivers a brief intervention based on the transtheoretical model of behavior change (TTM) to facilitate progress through the stages of change for quitting the most problematic drug and for seeking treatment if indicated. The prototype also delivers 30 days of stage-matched text messages and 4 Web-based activities addressing key topics. For providers, the clinical dashboard summarizes the patient's SUD risk scores and stage of change data, and provides stage-matched scripts to guide in-person sessions. METHODS: A total of 4 providers from 2 federally qualified health centers (FQHCs) were recruited for the pilot test, and they in turn recruited 5 patients with a known SUD. Furthermore, 3 providers delivered dashboard-guided SBIRT sessions and completed a brief acceptability survey. A total of 4 patients completed a Web-based SURI session and in-person SBIRT session, accessed other program components, and completed 3 acceptability surveys over 30 days. Questions in the surveys were adapted from the National Cancer Institute's Education Materials Review Form. Response options ranged from 1=strongly disagree to 5=strongly agree. The criterion for establishing acceptability was an overall rating of 4.0 or higher across items. RESULTS: For providers, the overall mean acceptability rating was 4.4 (standard deviation [SD] 0.4). Notably, all providers gave a rating of 5.0 for the item, "The program can give me helpful information about my patient." For patients, the overall mean acceptability rating was 4.5 (SD 0.3) for the mobile- and provider-delivered SBIRT sessions and 4.0 (SD 0.4) for the text messages and Web-based activities. One highly rated item was "The program could help me make some positive changes" (4.5). CONCLUSIONS: The SURI program and clinical dashboard, developed to reduce barriers to SBIRT in primary care, were well received by providers and patients.

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)0.007
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.020
GPT teacher head0.308
Teacher spread0.287 · 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 designObservational
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

Citations7
Published2018
Admission routes1
Has abstractyes

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