Virtual and Augmented Reality Applications for Promoting Safety and Security: A Systematic Review
Bibliographic record
Abstract
Virtual reality (VR) and augmented reality (AR) are known to offer an immersive environment that serves as a valuable tool in various domains such as education, health and wellness, and gaming. Due to its relevance in many areas, it is a technology worth exploring for promoting safety and security. This paper presents the results of a systematic review of the past 6 years of research (2016–2021) in the field of VR and AR for promoting safety and security. The goal is to (1) uncover the success and effectiveness of VR/AR technology in promoting safety and security, (2) identify the persuasive strategies employed in VR/AR for promoting safety and security, (3) uncover the limitations of existing AR/VR technology for promoting safety and security, and (4) discuss the prospects of VR/AR in promoting safety and security and recommend opportunities for future. To identify relevant papers, we searched 4 popular databases and screened them using a set of eligibility criteria included in the final review. The results showed that 26 of the papers indicated a successful outcome while 5 papers indicated a partially successful outcome with respect to promoting safety and security. Rehearsal and Self-monitoring strategies emerged as the most frequently used persuasive strategy in AR and VR applications for designs promoting safety and security.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".