What are the ethical issues related to telerehabilitation? A critical interpretive synthesis protocol
Bibliographic record
Abstract
INTRODUCTION: Telerehabilitation (also known as virtual rehabilitation) refers to the use of telecommunication technologies to deliver remote rehabilitation services synchronously or asynchronously to patients. Systematic reviews seem to validate the efficacy and efficiency of telerehabilitation services for diverse patient conditions while offering in addition potential cost savings in healthcare. However, integrating telerehabilitation into clinical settings raises several ethical issues, including the risk of exacerbating existing health inequities in the provision of care. Despite the apparent scarcity of the literature addressing ethical issues related to telerehabilitation, some of these fundamental concerns have already been discussed in health ethics publications. The main objectives of this study are therefore to first scrutinise what has been published to date and second to critically examine the way in which these dimensions have been conceptualised, especially the philosophical and ethical conceptions on which they are based. METHODS AND ANALYSIS: To meet these objectives, we will conduct a Critical Interpretive Synthesis (CIS). By using an iterative and interactive process, a CIS aims to critically examine the literature and develop a theoretical understanding grounded in review studies. As per the steps described by Dixon-Woods, we will start by conducting a systematic search of the literature within five selected databases: CINAHL, EMBASE, MEDLINE, Web of Science and PsycINFO. The search strategy will be based on two main concepts: (1) telerehabilitation and (2) ethics. This systematic search will be completed by other research strategies: searching the list of references of selected articles and contacting experts within and outside our team's expertise. Search results will be imported within the Covidence software to be assessed for relevance. We will include all empirical and non-empirical articles that specifically investigate or discuss the ethical dimensions of telerehabilitation. Only studies published in English and French will be included. The search and selection of the articles will be carried out interactively and inductively throughout the stages of extraction and development of a theoretical understanding of the data to fill emerging conceptual gaps. The analysis and critical synthesis will be led by the first author but carried out by our multidisciplinary research team. This study, through its critical dimension, has the potential to provide a more comprehensive overview of the many ethical issues surrounding telerehabilitation. ETHICS AND DISSEMINATION: This review does not require ethical approval. We aim to publish the results in a peer-reviewed journal and do presentations at local, national and/or international research meetings and workshops for all stakeholders.
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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 | 0.273 | 0.395 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.010 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.049 | 0.007 |
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".