Exploring Immersive Multimodal Virtual Reality Training, Affective States, and Ecological Validity in Healthy Firefighters: Quasi-Experimental Study
Bibliographic record
Abstract
BACKGROUND: Firefighters face stressful life-threatening events requiring fast decision-making. To better prepare for those situations, training is paramount, but errors in real-life training can be harmful. Virtual reality (VR) simulations provide the desired realism while enabling practice in a secure and controlled environment. Firefighters' affective states are also crucial as they are a higher-risk group. OBJECTIVE: To assess the impact on affective states of 2 simulated immersive experiences in a sample of healthy firefighters (before, during, and after the simulation), we pursued a multivariate approach comprising cognitive performance, situational awareness, depression, anxiety, stress, number of previous adverse events experienced, posttraumatic stress disorder (PTSD) severity, and emotions. The efficacy and ecological validity of an innovative VR haptic system were also tested, exploring its impact on performance. METHODS: In collaboration with the Portuguese National Fire Service School, we exposed 22 healthy firefighters to 2 immersive scenarios using the FLAIM Trainer VR system (neutral and arousing scenarios) while recording physiological data in a quasi-experimental study. Baseline cognitive performance, depression, anxiety, stress, number of adverse events, and severity of PTSD symptoms were evaluated. Positive and negative affective states were measured before, between, and after each scenario. Situational awareness, sense of presence, ecological validity, engagement, and negative effects resulting from VR immersion were tested. RESULTS: =0.39; P=.69). Participants' feedback on the VR sense of presence was also positive, reporting a high sense of physical space (mean score 3.9, SD 0.8), ecological validity (mean score 3.8, SD 0.6), and engagement (mean score 3.8, SD 0.6). Engagement was related to the number of previously experienced adverse events (r=0.49; P=.02) and positive affect (after the last VR task; r=0.55; P=.02). Conversely, participants reported few negative effects (mean score 1.7, SD 0.6). The negative effects correlated positively with negative affect (after the last VR task; r=0.53; P=.03); and avoidance (r=0.73; P<.001), a PTSD symptom, controlling for relevant baseline variables. Performance related to situational awareness was positive (mean 46.4, SD 34.5), although no relation was found to metacognitively perceived situational awareness (r=-0.12; P=.59). CONCLUSIONS: We show that VR is an effective alternative to in-person training as it was considered ecologically valid and engaging while promoting positive emotions, with few negative repercussions. This corroborates the use of VR to test firefighters' performance and situational awareness. Further research is needed to ascertain that firefighters with PTSD symptomatology are not negatively affected by VR. This study favors the use of VR training and provides new insights on its emotional and cognitive impact on the trainee.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".