Effectiveness of an interprofessional assessment and management approach for people with chronic low back disorders delivered via virtual care: A randomized controlled trial pilot intervention
Bibliographic record
Abstract
Objective: Virtual care for chronic conditions has seen uptake due to COVID-19. Evaluation of virtual models is important to ensure evidence-based practice. There is a paucity of research in the use of virtual care for management of chronic back disorders. The objective of this study was to evaluate effectiveness of a team-based virtual care model for back disorder assessment where a physical therapist uses virtual care to join a nurse practitioner and patient in a rural Saskatchewan, Canada community. Methods: = 20). The team-based care group involved a nurse practitioner located with a rural patient, and a physical therapist joining using virtual care. The physical therapist alone and the nurse practitioner alone groups received in-person assessments. Groups with a physical therapist involved had follow-up treatments by in-person physical therapy. Outcomes over six months included pain, disability, back beliefs, satisfaction, quality-adjusted health status and management-related costs. Results: There were no significant differences for pain, disability, back beliefs and satisfaction between groups. The average cost per patient for implementing in-person physical therapist assessment ($135) was higher compared with the team over virtual care ($118) and NP care ($59). Conclusion: Primary outcomes were not different by group. Physical therapist alone was more costly than other groups. Future research should include more participants, longer follow-up time and refined cost parameters. Trial Registration: ClinicalTrials.gov NCT02225535; https://clinicaltrials.gov/ct2/show/NCT02225535 (Archived by WebCite at http://www.webcitation.org/6lqLTCNF7).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".