A framework for equitable virtual rehabilitation in the metaverse era: challenges and opportunities
Bibliographic record
Abstract
Introduction: Metaverse technology is spurring a transformation in healthcare and has the potential to cause a disruptive shift in rehabilitation interventions. The technology will surely be a promising field offering new resources to improve clinical outcomes, compliance, sustainability, and patients' interest in rehabilitation. Despite the growing interest in technologies for rehabilitation, various barriers to using digital services may continue to perpetuate a digital divide. This article proposes a framework with five domains and elements to consider when designing and implementing Metaverse-based rehabilitation services to reduce potential inequalities and provide best patient care. Methods: The framework was developed in two phases and was informed by previous frameworks in digital health, the Metaverse, and health equity. The main elements were extracted and synthesized via consultation with an interdisciplinary team, including a knowledge user. Results: The proposed framework discusses equity issues relevant to assessing progress in moving toward and implementing the Metaverse in rehabilitation services. The five domains of the framework were identified as equity, health services integration, interoperability, global governance, and humanization. Discussion: This article is a call for all rehabilitation professionals, along with other important stakeholders, to engage in developing an equitable, decentralized, and sustainable Metaverse service and not just be a spectator as it develops. Challenges and opportunities and their implications for future directions are highlighted.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| 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".