Immigrant, Refugee, and Indigenous Canadians’ Experiences With Virtual Health Care Services: Rapid Review
Bibliographic record
Abstract
BACKGROUND: The remote, dispersed, and multicultural population of Canada presents unique challenges for health care services. Currently, virtual care solutions are being offered as an innovative solution to improve access to care. OBJECTIVE: Given the inequities in health care access faced by immigrant, refugee, and Indigenous Canadians, this review aimed to summarize information obtained from original research regarding these people's experiences with virtual care services in Canada. METHODS: We conducted a rapid review following published recommendations. MEDLINE and CINAHL were searched for studies relating to virtual care and Canadian immigrants, refugees, or Indigenous peoples. Peer-reviewed articles of any type were included so long as they included information on the experiences of virtual care service delivery in Canada among the abovementioned groups. RESULTS: This review demonstrates an extreme paucity of evidence examining the experiences of immigrant, refugee, and Indigenous groups with virtual care in Canada. Of the 694 publications screened, 8 were included in this review. A total of 2 studies focused on immigrants and refugees in Canada, with the remaining studies focusing on Indigenous communities. Results demonstrate that virtual care is generally accepted within these communities; however, cultural appropriateness or safety and inequitable access to wireless services in certain communities were among the most cited barriers. CONCLUSIONS: Little evidence exists outlining immigrants', refugees', and Indigenous peoples' perspectives on the landscape of virtual care in Canada. The development of virtual care programming should take into consideration the barriers, facilitators, and recommendations outlined in this review to improve equitable access. Further, developers should consult with local community members to ensure the appropriateness of services for immigrant, refugee, and Indigenous communities in Canada.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.022 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".