Nature-based mindfulness programs using virtual reality to reduce pediatric perioperative anxiety: a narrative review
Bibliographic record
Abstract
Over 75% of pediatric surgery patients experience preoperative anxiety, which can lead to complicated recoveries. Current interventions are less effective for children over 12 years old. New interventions, like mindfulness-based ones (MBIs), are needed to address this issue. MBIs work well for reducing mental health symptoms in youth, but they can be challenging for beginners. Virtual reality (VR) nature settings can help bridge this gap, providing an engaging 3-D practice environment that minimizes distractions and enhances presence. However, no study has investigated the combined effects of mindfulness training in natural VR settings for pediatric surgery patients, creating a significant gap for a novel intervention. This paper aims to fill that gap by presenting a narrative review exploring the potential of a nature-based mindfulness program using VR to reduce pediatric preoperative anxiety. It begins by addressing the risks of anxiety in children undergoing surgery, emphasizing its impact on physical recovery, and supporting the use of VR for anxiety reduction in hospitals. The review then delves into VR's role in nature and mindfulness, discussing theoretical concepts, clinical applications, and effectiveness. It also examines how the combination of mindfulness, nature, and VR can create an effective intervention, supported by relevant literature. Finally, it synthesizes the existing literature's limitations, findings, gaps, and contradictions, concluding with research and clinical implications.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".