Use of Virtual Care Strategies to Join Multidisciplinary Teams Evaluating Work-Related Injuries in Rural Residents
Bibliographic record
Abstract
Background: Rural injured workers requiring multidisciplinary assessments for musculoskeletal disorders face health access disparities, which include travel to urban centers. Virtual care can enhance access to multidisciplinary team care for musculoskeletal conditions in rural areas. Materials and Methods: A retrospective chart audit of 136 multidisciplinary assessment reports of injured workers was conducted. Comprehensive management recommendations from the health care assessment team were extracted for analysis. The health care team used virtual technologies to join with patients and at least one local rural health practitioner in one of three locations. Remote presence robotics (RPR; Xpress Technology™) or laptop-based telehealth was used to complete the assessments. Results: RPR were used in 46% of assessments over two sites, with 54% using laptop-based telehealth at a third site. Frequencies of team members' assessment using technologies were as follows: physical therapist (100%), psychologist (78%), plastic surgeon (8%), and physician (43%). Spine (42%) and shoulder (32%) disorders were the most common problems. Most workers (79%) were 3 or more months postinjury. The most common management recommendation was the need for daily comprehensive rehabilitation care (76%). Travel time was saved by 89% of participants. Conclusions: Virtual care was used to unite multidisciplinary assessment teams for the evaluation of injured rural workers with complex musculoskeletal injuries. Future research recommendations include comparing between virtual and fully in-person multidisciplinary assessment and recommendation findings, and evaluation of patient and practitioner experiences with comprehensive virtual team assessments.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".