Case Report: Using a Remote Presence Robot to Improve Access to Physical Therapy for People with Chronic Back Disorders in an Underserved Community
Bibliographic record
Abstract
Purpose: The purpose of this case study was to evaluate the delivery of an inter-professional (IP) spinal triage management approach to chronic back disorders using remote presence robotic technology as an innovative form of telerehabilitation in a northern Saskatchewan community. Methods: The IP team, consisting of a local nurse practitioner (NP) and a physical therapist in an urban centre, completed a comprehensive neuromusculoskeletal assessment of, and one follow-up visit with, a post-surgical spinal patient. Treatment included detailed education regarding self-management after spinal surgery, provision and progression of home exercises, and reassurance. The patient was then referred to regional, in-person physical therapy care to complete her treatment. Results: A semi-structured interview with the NP revealed a high level of satisfaction; qualitative themes included the value of IP practice and the benefit to the patient of telerehabilitation achieved through patient-centred care. In a post-treatment survey, the patient expressed a high level of satisfaction with and appreciation for the patient-centred approach and the IP team. Objective clinical improvements in spinal and straight-leg raise movements were noted at the final telerehabilitation session. Conclusion: This report demonstrates the feasibility of delivering IP spinal triage management using telerehabilitation, specifically remote presence robotics, in a remote setting. Further research should include larger scale studies that investigate health, system, and economic outcomes as well as comparative studies for other forms of telehealth technology.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 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".