Ethical Challenges of Virtual Reality Technology Interventions for the Vulnerabilities of Patients With Chronic Pain: Exploration of Technician Responsibility
Bibliographic record
Abstract
Chronic pain, a common disease, is a crucial global public health concern. Approximately 20% of the worldwide population is affected by chronic pain, which accounts for 15% to 20% of hospital visits. In Canada, approximately 7.6 million people-or 1 in 5 people-experience chronic pain. Among this population, 60% has either lost their employment or experienced a reduction in income as a result of their pain. The proportion of older people (aged ≥65 years) with chronic pain is high, comprising one-third of the total older population. In addition, the causes of chronic pain and its cures are unknown, and treatment is limited by these unknowns and the dangers of opioids. These essential factors make patients with chronic pain one of the most vulnerable populations. The use of emerging virtual reality (VR) technology as an intervention for chronic pain has consistently demonstrated early effectiveness and has been termed as a "nonpharmacological analgesic." Nevertheless, we must remain vigilant about the potential ethical risks of VR interventions, as inappropriate VR interventions may exacerbate the vulnerabilities of patients. Currently, a central challenge for VR developers is the ambiguity of patient vulnerability and the unpredictability of ethical dilemmas. Therefore, our paper focused on the vulnerability and ethical dilemmas faced by patients with chronic pain in VR interventions. Through an experience-based, prospective ethical examination, we have identified both existing and potential new vulnerabilities and specific manifestations that patients with chronic pain may encounter in VR interventions. Our aim was to highlight the ethical risks that may be present in VR interventions. On one hand, this can help raise awareness among technology developers regarding the vulnerabilities of patients with chronic pain and mitigate technological ethical risks. In addition, it can assist technology developers in determining the priorities for VR technology interventions. These efforts collectively lay a solid foundation for the comprehensive realization of responsible VR technology interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.070 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".