A Model to Implement Standardized Virtual Care for Low Back Pain Amongst a Large Network of Providers in Urban and Rural Settings
Bibliographic record
Abstract
Prior to the COVID-19 pandemic, virtual care (VC) was not routinely offered for assessment of low back pain (LBP), a highly prevalent, disabling condition. COVID-19 related healthcare closures resulted in a rapid backlog of patients referred to a provincial interprofessional LBP program. Without management, these patients were at high risk of experiencing untoward outcomes. Virtual care became a logical option. However, many clinicians lacked experience and confidence with LBP virtual care (LBP-VC); and either were unfamiliar with, or did not have access to, requisite technology. Multi-stakeholder engagement was utilized to understand barriers, identify enablers, and ultimately promote VC for LBP. As a result of the multi-stakeholder engagement, the concept of a toolkit for LBP-VC, including clinical resources and guidelines, emerged. The toolkit contains preparatory steps for VC and a standardized approach to virtual LBP assessment. Key steps in the toolkit have potential applicability to other musculoskeletal populations.
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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 | 0.006 | 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 | 0.001 |
| 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".