Virtual Care for Indigenous Populations in Canada, the United States, Australia, and New Zealand: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Indigenous people in Canada, the United States, Australia, and New Zealand experience an increased burden of chronic diseases compared to non-Indigenous people in these countries. Lack of necessary services and culturally relevant care for Indigenous people contributes to this burden. Many Indigenous communities have implemented systems, such as virtual care, to improve chronic disease management. Virtual care has extended beyond videoconferencing to include more advanced technologies, such as remote biometric monitoring devices. However, given the historical and ongoing Western intrusion into Indigenous day to day life, these technologies may seem more invasive and thus require additional research on their acceptability and utility within Indigenous populations. OBJECTIVE: The objective of this paper is to present the protocol for a scoping review, which aims to map existing evidence. This study is based on the following guiding research question: What are the characteristics of virtual care use by Indigenous adult populations in Canada, the United States, Australia, and New Zealand? The subquestions are related to the technology used, health conditions and nature of the virtual care, cultural safety, and key concepts for effective use. METHODS: This scoping review protocol is informed by the methodology described by the Joanna Briggs Institute and is supplemented by the frameworks proposed by Arksey and O'Malley and Levac et al. A search for published and gray literature, written in English, and published between 2000 and present will be completed utilizing electronic databases and search engines, including MEDLINE, CINAHL, Embase, Indigenous Peoples of North America, Australian Indigenous HealthInfoNet, Informit, and Native Health Database. Search results will be uploaded to the review software, Covidence, for title and abstract screening before full-text screening begins. This process will be repeated for gray literature. Upon completion, a data abstraction tool will organize the relevant information into categorical formations. RESULTS: The search strategy has been confirmed, and the screening of titles and abstracts is underway. As of October 2020, we have identified over 300 articles for full-text screening. CONCLUSIONS: Previous reviews have addressed virtual care within Indigenous communities. However, new virtual care technologies have since emerged; subsequently, additional literature has been published. Mapping and synthesizing this literature will inform new directions for research and discussion. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/21860.
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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 | 0.107 | 0.124 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.024 | 0.024 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.050 | 0.008 |
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".