Access, Relationships, Quality and Safety (ARQS): a qualitative study to cocreate an Indigenous patient experience tool for virtual primary care
Bibliographic record
Abstract
BACKGROUND: Perspectives from Indigenous peoples and their primary care providers about the quality and impacts of virtual primary care for Indigenous patients are currently limited. This study engaged Indigenous patients and their primary care providers, resulting in four domains being established for an Indigenous patient experience tool for use in virtual primary care. In this paper, we explore the development and finalisation of the Access, Relationships, Quality and Safety (ARQS) tool. METHODS: We re-engaged five Indigenous patient participants who had been involved in the semistructured interviews that established the ARQS tool domains. Through cognitive interviews, we tested the tool statements, leading to modifications. To finalise the tool statements, an Indigenous advisory group was consulted. RESULTS: The ARQS tool statements were revised and finalised with twelve statements that reflect the experiences and perspectives of Indigenous patients. DISCUSSION: The ARQS tool statements assess the four domains that reflect high-quality virtual care for Indigenous patients. By centring Indigenous peoples and their lived experience with primary care at every stage in the tool's development, it captures Indigenous-centred understandings of high-quality virtual primary care and has validity for use in virtual primary care settings. CONCLUSION: The ARQS tool offers a promising way for Indigenous patients to provide feedback and for clinics to measure the quality and safety of virtual primary care practice on the provider and/or clinic level. This is important, as such feedback may help to promote improvements in virtual primary care delivery for Indigenous patients and more widely, may help advance Indigenous health equity.
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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 | 0.024 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".