The use of extended reality (XR) in patient education: A critical perspective
Bibliographic record
Abstract
Objective: Extended reality (XR) has emerged as an innovative educational modality that offers the potential for the creation of more interactive and engaging forms of patient education experiences and products. The purpose of this article is to describe the field of XR technologies and review its potential through a critical lens as well as its possible adoption as a mainstream technology for providing patient education in the future. Method: A review of the literature was undertaken to summarise the emerging evidence concerning the effectiveness of XR as a patient education modality. The findings of several reviews are summarised and a critical discussion of potential issues and challenges in the adoption and use of XR among particular marginalised populations are explored. Results: The emerging evidence suggests that different forms of XR technology applications have the potential to create immersive and engaging patient education experiences that can lead to enhanced patient satisfaction, positive educational outcomes and reduced patient anxiety. Nonetheless, there have been calls for greater consideration of how patient characteristics, including socioeconomic status, gender, cultural and generational differences, influence the learning effects of virtual reality educational applications, as well as its adoption and implementation for patient education purposes. Conclusion: The evidence surrounding the effectiveness of XR in patient education is growing; however, various factors could influence the successful adoption and implementation of XR in different patient populations who have traditionally experienced challenges with digital health literacy. The paper offers some recommendations for enhancing the evidence base and potential approaches to advance the design and evaluation of XR applications in patient education.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| 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".