Access, relationships, quality and safety (ARQS): a qualitative study to develop an Indigenous-centred understanding of virtual care quality
Bibliographic record
Abstract
BACKGROUND: Among Indigenous peoples in Canada, access to high-quality healthcare remains an important determinant of health. The shift to virtual and remote-based approaches, expedited during the COVID-19 pandemic, influenced the ways in which individuals accessed care and the quality of care received. This study sought to determine which elements are required for effective and sustainable virtual care approaches for delivery of primary care to Indigenous patients and develop quality indicators grounded in Indigenous community and experience. We share a conceptual framework to understand how Indigenous patients access and define high-quality virtual care, grounded in Indigenous patient experiences and worldviews. METHODS: Using principles of patient-oriented research, we grounded this work in social justice and participatory action research. We sought to gain an in-depth understanding of the Indigenous experiences of virtual care and specifically of primary care. This was developed through semistructured interviews with Indigenous patients and Indigenous virtual primary care providers. RESULTS: Thirteen participants were interviewed between 5 August 2021 and 25 October 2021. Using Framework Analysis, we constructed four domains including access, relationships, quality and safety as being primary facets of defining high-quality Indigenous virtual primary care. DISCUSSION: The results presented here indicate that the shift to virtual care, largely seen in response to the COVID-19 pandemic, does not compromise quality of care, nor does it lead to negative patient experiences. Optimal care is possible in virtual settings for some care needs and types of appointments and has the potential to decrease barriers to access and improve patient experiences of safety and quality while facilitating patient/provider relationships. CONCLUSION: In summary, high-quality Indigenous virtual care benefits from attention to patients' experiences of access, relationships, safety and quality with their service providers and healthcare teams.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".