Natural Scene Virtual Reality as a Behavioral Health Countermeasure in Isolated, Confined, and Extreme Environments: Three Isolated, Confined, Extreme Analog Case Studies
Bibliographic record
Abstract
INTRODUCTION: Isolated, confined, extreme (ICE) environments are accompanied by a host of stress-inducing circumstances: operational pressure, interpersonal dynamics, limited communication with friends and family, and environmental hazards. We evaluated the effectiveness of attention-restoration-therapy-based immersive Virtual Reality (VR) in three ICE environments: the Canadian Forces Station-Alert (CFS Alert), the 12-month HI-SEAS IV expedition, and the 8-month HI-SEAS V expedition. METHODS: Thirty-one individuals (29 male, 2 female) at CFS Alert, and 12 total crewmembers (7 male, 5 female, six crewmembers per sessions) at HI-SEAS participated. All participants viewed immersive VR scenes, but scene content varied by deployment. Data collection included pre- and post-intervention surveys and semi-structured post-mission interviews. Survey data were analyzed by scene content within each analog using nonparametric approaches. RESULTS: Acceptability and desirability of the VR content varied significantly by ICE analog, as well as by participants within a given analog. The two initial exploratory protocols enabled a more directed study in HI-SEAS V to identify the importance of differences in scene content. DISCUSSION: Use and perceived utility of the VR varied considerably across participants, indicating that psychological support needs to be individualized. Overall, natural scene VR was broadly considered restorative, but after long periods of isolation, dynamic and familiar scenes including those with people were also appealing. Immersive, nature-based VR was highly valued by some, but not all participants, suggesting that this intervention tool holds promise for use in ICE settings but needs to be tailored to the setting and individual.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".