Provider perspectives on safety of telerehabilitation and risks of nonphysical harm for neurological conditions: a qualitative descriptive study
Bibliographic record
Abstract
PURPOSE: Safety in telerehabilitation, especially regarding nonphysical harm, remains underexplored. This study examines providers' perspectives on the risks, challenges, and strategies to enhance safety in telerehabilitation for individuals with neurological conditions. METHODS: Using a qualitative descriptive approach, we invited 25 outpatient telerehabilitation providers, leaders and learners from a large academic rehabilitation hospital in four focus groups and three interviews to share their experiences about safety in their telerehabilitation practice. Discussions were audio-recorded, transcribed, and analyzed using an inductive thematic approach. RESULTS: Three overarching themes emerged: (1) Despite their neurological complexity, patients in this study were largely safe, comfortable, and confident participating in telerehabilitation at home. (2) Providers themselves may be vulnerable to under-recognized nonphysical harm, reflected in three sub-themes: persistent worry about patient safety, moral and ethical distress over perceived care inequities, and blurred professional boundaries. (3) Strategies to mitigate harm included recognizing and addressing provider nonphysical harm as essential to advancing to safety and wellness, with opportunities such as standardized education, peer support, and effective patient triage. CONCLUSION: As virtual care expands, understanding the risks of nonphysical harm to telerehabilitation providers is critical as part of a broader, more inclusive approach to safety and wellness.
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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".