Using VR-based interventions, wearable technology, and text mining to improve military and Veteran mental health
Bibliographic record
Abstract
Introduction: Virtual reality (VR)-based interventions, wearable technology and text mining hold promising potential for advancing the way in which military and Veteran mental health conditions are diagnosed and treated. They have the ability to improve treatment protocol adherence, assist in the detection of mental health conditions, enhance resilience and increase a patient’s motivation to continue therapy. Methods: This article explores five cutting-edge research projects designed to leverage VR-based interventions, wearable technology, and text mining to improve military and Veteran mental health. A computer-animated virtual agent provides online coaching for posttraumatic stress disorder (PTSD) patients in their own homes to enhance treatment compliance. A head-mounted display safely immerses PTSD patients in a virtual world to relive past experiences and associate them with new meanings. Gaming and simulation technology are tested as a way to improve resilience and performance in military members in deployment-related scenarios. Guidelines are developed for the creation of wearable assistive technology for military members and Veterans. Text mining is explored as a way to assist in the detection of PTSD. Results: VR-based therapy, gaming and simulation, wearable assistive and sensory technology, and text mining hold promise for diagnosing, monitoring, and treating military mental health conditions. Discussion: The five research projects presented have made promising contributions to the field of military and Veteran mental health, either by advancing diagnostic trajectories, contributing to therapy or enhancing the process by developing new approaches to delivering preventive or curative care.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".