Will virtual rehabilitation replace clinicians: a contemporary debate about technological versus human obsolescence
Bibliographic record
Abstract
This article is inspired by a pseudo Oxford-style debate, which was held in Tel Aviv University, Israel at the International Conference on Virtual Rehabilitation (ICVR) 2019, which is the official conference of the International Society for Virtual Rehabilitation. The debate, between two 2-person teams with a moderator, was organized by the ICVR Program committee to address the question "Will virtual rehabilitation replace clinicians?" It brought together five academics with technical, research, and/or clinical backgrounds-Gerry Fluet, Tal Krasovsky, Anat Lubetzky, Philippe Archambault, W. Geoffrey Wright-to debate the pros and cons of using virtual reality (VR) and related technologies to help assess, diagnose, treat, and track recovery, and more specifically investigate the likelihood that advanced technology will ultimately replace human clinicians. Both teams were assigned a side to defend, whether it represented their own viewpoint or not, and to take whatever positions necessary to make a persuasive argument and win the debate. In this paper we present a recapitulation of the arguments presented by both sides, and further include an in-depth consideration of the question. We attempt to judiciously lay out a number of arguments that fall along a spectrum from moderate to extreme; the most extreme and/or indefensible positions are presented for rhetorical and demonstrative purposes. Although there may not be a clear answer today, this paper raises questions which are related to the basic nature of the rehabilitation profession, and to the current and potential role of technology within it.
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 | 0.064 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.090 |
| Scholarly communication | 0.024 | 0.029 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.032 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 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".