Nature-based mindfulness-compassion programs using virtual reality for older adults: A narrative literature review
Bibliographic record
Abstract
The global population is aging at an unprecedented rate, increasing the necessity for effective interventions targeting the mental health needs of older adults. Technology addressing the aging process of older adults (i.e., gerontechnology) is an avenue for the efficient delivery of programs that enhance adult well-being. Virtual reality (VR) is a type of gerontechnology with the potential to improve mental health and well-being (e.g., by increasing resilience, mindfulness, compassion, connection with nature, and decreasing stress, depression, anxiety); however, evidence in this area is currently lacking and more rigorous research on the acceptability, feasibility, and effectiveness of mental health programming via VR for older adults, such as nature, mindfulness, or compassion-based interventions, is necessary. The present literature review: 1) explores, synthesizes, and critically evaluates the literature on older adult mental health, well-being and gerontechnology, with a focus on virtual reality-based nature, mindfulness, and compassion-based interventions; 2) examines research to date on the relationship between virtual reality technology and nature, mindfulness, and self-compassion; 3) identifies gaps, contradictions, and limitations of existing research; 4) identifies areas for further investigation; and 5) discusses implications for research and clinical practice.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".