Innovative Virtual Role Play Simulations for Managing Substance Use Conversations: Pilot Study Results and Relevance During and After COVID-19
Bibliographic record
Abstract
BACKGROUND: Substance use places a substantial burden on our communities, both economically and socially. In light of COVID-19, it is predicted that as many as 75,000 more people will die from alcohol and other substance use and suicide as a result of isolation, new mental health concerns, and various other stressors related to the pandemic. Public awareness campaigns that aim to destigmatize substance use and help individuals have meaningful conversations with friends, coworkers, or family members to address substance use concerns are a timely and cost-effective means of augmenting existing behavioral health efforts related to substance use. These types of interventions can supplement the work being done by existing public health initiatives. OBJECTIVE: This pilot study examines the impact of the One Degree: Shift the Influence role play simulation, designed to teach family, friends, and coworkers to effectively manage problem-solving conversations with individuals that they are concerned about regarding substance use. METHODS: Participants recruited for this mixed methods study completed a presurvey, the simulation, and a postsurvey, and were sent a 6-week follow-up survey. The simulation involves practicing a role play conversation with a virtual human coded with emotions, a memory, and a personality. A virtual coach provides feedback in using evidence-based communication strategies such as motivational interviewing. RESULTS: A matched sample analysis of variance revealed significant increases at follow-up in composite attitudinal constructs of preparedness (P<.001) and self-efficacy (P=.01), including starting a conversation with someone regarding substance use, avoiding upsetting someone while bringing up concerns, focusing on observable facts, and problem solving. Qualitative data provided further evidence of the simulation's positive impact on the ability to have meaningful conversations about substance use. CONCLUSIONS: This study provides preliminary evidence that conversation-based simulations like One Degree: Shift the Influence that use role play practice can teach individuals to use evidence-based communication strategies and can cost-effectively reach geographically dispersed populations to support public health initiatives for primary prevention.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".