First Nations People Perspectives on Telehealth Physiotherapy: A Qualitative Study Focused on the Therapeutic Relationships
Bibliographic record
Abstract
Introduction: Relationships are the core of Indigenous peoples' identity, and therapeutic relationships (TRs) are an integral part of the physical rehabilitation process, directly influencing health outcomes. However, participating in TRs can be difficult for First Nations People, particularly in the virtual landscape. Therefore, the purpose of this study is to explore First Nations People’s perspective on telehealth physiotherapy, with an emphasis on the virtual therapeutic relationship. Methods: A narrative qualitative study which utilised 1:1, semi-structured interviews were conducted with nineteen First Nations adults from remote and rural First Nation communities in British Columbia, Canada. Interviews were recorded, transcribed verbatim, and analyzed using reflexive thematic analysis. Results: Three themes emerged from data analysis. Therapist’s attitude and rapport captured participants’ perceptions of what matters the most in how physiotherapists relate to First Nation People and carry out their work. Remote nature of virtual care encompassed the main challenges of virtual care visits, particularly how these were perceived to impact establishing and maintaining solid therapeutic relationships. Fostering culturally appropriate and safe telehealth physiotherapy focuses on what is needed to advance telehealth physiotherapy in a manner that respects and reflects First Nations cultures, equipping all involved parties to provide comprehensive and sensitive services. Conclusion: The pursuit of equitable healthcare for First Nations communities demands access and a thoughtful, culturally safe, trauma-informed, and holistic approach that addresses their needs.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".