A remote first nation community-informed virtual care approach to chronic back pain management: a mixed methods study
Bibliographic record
Abstract
Chronic back pain (CBP) is a widespread public health issue. There is a lack of community-based care options for Indigenous Peoples with CBP. A virtual care clinic co-designed with community was implemented in the Cree Community of Pelican Narrows using remote presence (RPR) technology. Methods We used a community-based participatory action framework and pre-post design to evaluate this intervention. Pain, quality of life and experience outcomes were measured. An assessment was provided by a local nurse practitioner and a physical therapist joining over RPR. The physical therapist provided jp to four follow-up treatments per participant using RPR. Results Thirty-eight participants were assessed, and 78 follow-up treatments delivered. No significant differences between pre- and post- pain or quality of life were found. Thirteen patient participants and five health providers completed interviews. Patient themes included: (1) community and healthcare context, (2) community preferences for back pain care, and (3) experience with virtual back pain clinic. Health care provider themes included: (1) getting people to clinic, (2) experience with virtual back pain clinic, and (3) ways to enhance care. Conclusion Virtual CBP clinic enhanced access to therapy and was experienced positively. Participants suggested ways to address challenges, including a hybrid model of 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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".