Physical Therapists' Perspectives on the Use of Telehealth With First Nations Peoples in Canada: A Qualitative Study
Bibliographic record
Abstract
OBJECTIVE: This study aims to explore physical therapists' perspectives on providing physical therapy to First Nations peoples in Canada via telehealth, specifically to understand (1) their perspectives on the feasibility of telehealth as a medium for health care delivery and (2) their experiences building trusting therapeutic relationships via telehealth care. METHODS: This study included 13 physical therapists who provided clinical care via telehealth for First Nations individuals in northern British Columbia in the past 3 years, and 7 master of physical therapy students undergoing or who completed their Indigenous Health clinical placement in the past 3 years and utilized telehealth. In-depth semi-structured interviews were conducted. Interview questions explored: telehealth usage and acceptance, experiences with telehealth, and overall recommendations for telehealth. Audio recordings were transcribed, and reflexive thematic analysis was conducted. RESULTS: Three overarching themes were identified: "Telehealth can make a huge difference, but it is widely underutilized;" "Telehealth is a little bit less personal and in-depth;" and "There is a time and place for telehealth." CONCLUSION: Telehealth shows promise in the delivery of physical therapy to First Nations communities in Canada, enhancing accessibility, offering flexible scheduling options, and optimizing therapist time efficiency. However, successful implementation in these communities is contingent upon addressing several challenges, including building trusting therapeutic relationships. Technological glitches, the absence of physical interaction, and a history of trauma may hinder the development of the therapeutic relationship in telehealth encounters. To mitigate these challenges, cultural safety training, initial in-person appointments, community familiarity, and in-person support from a caregiver or other health care provider may play pivotal roles. IMPACT: This study not only sheds light on the underutilization of telehealth but also underscores its potential to significantly improve the accessibility and efficiency of physical therapy to First Nations peoples in Canada. The findings emphasize the nuanced dynamics of therapeutic relationships in telehealth, offering critical insights for the integration of culturally sensitive practices. Addressing the challenges pinpointed can enhance the quality of telehealth care for First Nations individuals, promote more equitable health care delivery, and foster positive health outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".