Virtual Worlds Technology to Enhance Training for Primary Care Providers in Assessment and Management of Posttraumatic Stress Disorder Using Motivational Interviewing: Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Many individuals with posttraumatic stress disorder (PTSD) first present to primary care rather than specialty mental health care. Primary care providers often lack the training required to assess and treat patients with PTSD. Virtual trainings have emerged as a convenient and effective way of training primary care providers in PTSD assessment and communication methods (ie, motivational interviewing [MI]). OBJECTIVE: The aim of this study was to conduct a pilot randomized controlled trial of a synchronous Virtual Worlds (VW; a virtual world where learners were immersed as avatars) training versus an asynchronous web-based training on PTSD and MI, comparing the feasibility, acceptability, usability, and preliminary efficacy of 2 different training platforms among primary care providers. METHODS: Participating primary care providers were randomized to a VW and a web-based PTSD training. Outcomes were collected at baseline, posttraining, and 90-days follow-up. Standardized patient interviews measured participants' communication skills in assessing and managing patients with PTSD symptoms. RESULTS: Compared to the web-based training, the VW training platform achieved larger learning gains in MI (ie, partnership and empathy) and in discussing pharmacotherapy and psychotherapy for PTSD. Both VW and web-based trainings led to increases in PTSD knowledge and primary care providers' self-confidence. CONCLUSIONS: The asynchronous web-based PTSD training improved PTSD-related knowledge and self-confidence but was not as effective as the VW immersive experience in teaching MI or clinical management. Because VW training is synchronous and new for many learners, it required more time, facilitation, and technical support. As computer technology improves, VW educational interventions may become more feasible, particularly in teaching clinical skills. TRIAL REGISTRATION: ClinicalTrials.gov NCT03898271; https://tinyurl.com/mu479es5.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".