Experience of patients and practitioners with a team and technology approach to chronic back disorder management
Bibliographic record
Abstract
Stacey Lovo,1 L Harrison,1 ME O’Connell,2 C Trask,3 B Bath1,3 1School of Rehabilitation Science, University of Saskatchewan, Saskatoon, SK S7N 2Z4, Canada; 2College of Arts and Science, University of Saskatchewan, Saskatoon, SK S7N 5A5, Canada; 3Canadian Centre for Health and Safety in Agriculture, College of Medicine, University of Saskatchewan, Saskatoon, SK S7N 2Z4, CanadaCorrespondence: Stacey LovoSchool of Rehabilitation Science, University of Saskatchewan, Health Sciences Building, E-Wing Suite 3400, 3rd Floor, 104 Clinic Place, Saskatoon, SK S7N 2Z4, CanadaTel +1 306 241 9545Email stacey.lovo@usask.caPurpose: Although rural and remote residents face general challenges accessing health care in comparison to urban dwellers, care for musculoskeletal conditions like chronic back disorders (CBD) is particularly challenging for rural and remote residents due to lack of access to physical yherapists. Telerehabilitation such as secure videoconferencing offers one solution to this disparity in rural care delivery, but incorporating the perspectives of health practitioners and patients is important when developing new sustainable care models.Patients and methods: This study investigated the experiences of practitioners and patients during a novel interprofessional model of assessment where an urban-based physical therapist used videoconferencing to virtually join a rural nurse practitioner and a rural patient with CBD. Patient surveys and semi-structured interviews of practitioners and patients were analyzed quantitatively and qualitatively.Results: Most patients were “very satisfied” (62.1%) or “satisfied” (31.6%) with the overall experience, and “very” (63.1%) or “somewhat (36.9%) confident” with the assessment. Thematic analysis of interviews revealed that this novel assessment method identified: access to care for CBD, effective interprofessional practice, enhanced clinical care for CBD, and technology considerations.Conclusion: Patient satisfaction with the telerehabilitation model of care was high. Patients and practitioners reported their experiences were impacted by access to care, interprofessional practice, enhanced care for CBD and technology. These findings will be useful in the development of patient-centered models of care utilizing telehealth strategies.Keywords: telemedicine, interprofessional relations, low back pain, physical therapy, patient-centered care
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".