Understanding Virtual Primary Healthcare with Indigenous Populations: A Rapid Evidence Review
Bibliographic record
Abstract
Abstract Background: Virtual care has become an increasingly useful tool for the virtual delivery of care across the globe. With the unexpected emergence of COVID-19 and ongoing public health restrictions, it has become evident that the delivery of high-quality telemedicine is critical to ensuring the health and wellbeing of Indigenous peoples, especially those living in rural and remote communities. Methods: We conducted a rapid evidence review from August to December 2021 to understand how high quality Indigenous primary healthcare is defined in virtual modalities. After completing data extraction and quality appraisal, a total of 20 articles were selected for inclusion. The following question was used to guide the rapid review: How is high quality Indigenous primary healthcare defined in virtual modalities? Results: We discuss key limitations to the delivery of virtual care, including the increasing cost of technology, lack of accessibility, challenges with digital literacy, and language barriers. This review further yielded three main themes that highlight Indigenous virtual primary healthcare quality: (1) the importance of Indigenous-centred virtual care, (2) virtual Indigenous relationality and the building of trust, and (3) collaborative approaches to ensuring holistic virtual care. Discussion: For virtual care to be Indigenous-centred, Indigenous leadership and users need to be partners in the development, implementation and evaluation of the intervention, service or program. In terms of virtual models of care, time must be allocated to educate Indigenous partners on digital literacy, virtual care infrastructure, benefits and limitations. Relationality and culture must be prioritized as well as digital health equity. Conclusion: These findings highlight important considerations for strengthening virtual primary healthcare approaches to meet the needs of Indigenous peoples worldwide.
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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 | 0.027 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".