Development and Use of Health-Related Technologies in Indigenous Communities: Critical Review
Bibliographic record
Abstract
BACKGROUND: Older Indigenous adults encounter multiple challenges as their age intersects with health inequities. Research suggests that a majority of older Indigenous adults prefer to age in place, and they will need culturally safe assistive technologies to do so. OBJECTIVE: The aim of this critical review was to examine literature concerning use, adaptation, and development of assistive technologies for health purposes by Indigenous peoples. METHODS: Working within Indigenous research methodologies and from a decolonizing approach, searches of peer-reviewed academic and gray literature dated to February 2016 were conducted using keywords related to assistive technology and Indigenous peoples. Sources were reviewed and coded thematically. RESULTS: Of the 34 sources captured, only 2 concerned technology specifically for older Indigenous adults. Studies detailing technology with Indigenous populations of all ages originated primarily from Canada (n=12), Australia (n=10), and the United States (n=9) and were coded to four themes: meaningful user involvement and community-based processes in development, the digital divide, Indigenous innovation in technology, and health technology needs as holistic and interdependent. CONCLUSIONS: A key finding is the necessity of meaningful user involvement in technology development, especially in communities struggling with the digital divide. In spite of, or perhaps because of this divide, Indigenous communities are enthusiastically adapting mobile technologies to suit their needs in creative, culturally specific ways. This enthusiasm and creativity, coupled with the extensive experience many Indigenous communities have with telehealth technologies, presents opportunity for meaningful, culturally safe development processes.
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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 | 0.022 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".