A Digital Tool to Promote Alcohol and Drug Use Screening, Brief Intervention, and Referral to Treatment Skill Translation: A Mobile App Development and Randomized Controlled Trial Protocol
Bibliographic record
Abstract
BACKGROUND: Translation of knowledge and skills from classroom settings to clinical practice is a major challenge in healthcare training, especially for behavioral interventions. For example, screening, brief intervention, and referral to treatment (SBIRT) is a highly-promoted approach to identifying and treating individuals at risk for alcohol or drug problems, yet effective, routine use of SBIRT has lagged. OBJECTIVE: The objective of this paper is to describe the development, pilot testing, and trial protocol of a mobile app based on the theory of planned behavior (TPB) to promote SBIRT skill translation and application. METHODS: Intended for use after classroom training occurs, the mobile app has three primary functions designed to increase behavioral intent to deliver SBIRT: (1) review skills (ie, address knowledge and beliefs about SBIRT), (2) apply skills with patients (ie, build confidence and perceived behavioral control), and (3) report performance data (ie, increase accountability and social norms and/or influence). The app includes depression and anxiety screening tools due to high comorbidity with substance use. A randomized controlled trial (RCT) is in progress among health and social service learners (N=200) recruited from 3 universities and 6 different training programs in nursing, social work, internal medicine, psychiatry, and psychology. Participants are randomized to SBIRT classroom instruction alone or classroom instruction plus app access prior to beginning their field placement rotations. TPB-based data are collected via Qualtrics or via the mobile app pre-post and SBIRT utilization, weekly for 10 weeks. Key outcomes include the frequency of and self-reported confidence in delivery of SBIRT. RESULTS: Beta testing with advanced practice nursing students (N=22) indicated that the app and its associated assessment tools were acceptable and useful. The system usability scale (SUS) mean was 65.8 (n=19), which indicated that the SBIRT app was acceptable but could benefit from improvement. Indeed, modifications were implemented prior to starting the trial. Enrollment of trial participants began in September 2016. Results are expected by December 2017. CONCLUSIONS: This report describes the process of TPB-based app development and testing, and the protocol for a RCT to determine the effectiveness of the app in enhancing skill translation. If effective, this approach could improve SBIRT implementation, fidelity, and clinical outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.023 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.070 | 0.009 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".