Selection of Key Stressors to Develop Virtual Environments for Practicing Stress Management Skills with Military Personnel Prior to Deployment
Bibliographic record
Abstract
Virtual environments (VEs) are presently being used to treat military personnel suffering from posttraumatic stress disorder (PTSD). In an attempt to reduce the risk of PTSD, VEs may also be useful for stress management training (SMT) to practice skills under stress, but such use necessitates the development of relevant stress-inducing scenarios and storyboards. This article describes the procedures followed to select which VEs could be built for the Canadian Forces. A review and analysis of the available literature and of data collected postdeployment from 1,319 respondents on the frequency of stressors and their association with psychological injuries were pulled together to propose eight potential virtual stressors that can be used to practice SMT: seeing dead bodies or uncovering human remains; knowing someone being seriously injured or killed; receiving artillery fire; being unable to help ill or wounded civilians because of the rules of engagement; seeing destroyed homes and villages; clearing and searching homes, caves, or bunkers; receiving small-arms fire; and participating in demining operations. Information reported in this article could also be useful to document traumatic stressors experienced in theater of operations and their potential impact on psychological injuries.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".