Health Programming to Support Remote Indigenous Older Adults with Technology Use in Saskatchewan
Bibliographic record
Abstract
Colonial legacies have left Indigenous older adults in remote communities vulnerable to health challenges that impede healthy ageing in place. Access to culturally safe health programming and support for using ageing technology remains a significant challenge for these older adults. Our previous research projects within the Star Blanket Cree Nation introduced older adults to new technologies, including blood glucose monitors, blood pressure monitors, smart scales, tablets, and Fitbits. The Community Knowledge Council, which directs this research, has identified a significant gap in further support and health programming around these technologies that must be addressed. Consequently, the Community Knowledge Council has recommended engaging older adults through culturally safe health programming focused on technology use. Using an Indigenist theoretical framework and a community-based participatory research approach, this research project aims to support Indigenous older adults in maximizing existing technologies through culturally safe health programming. Sharing circles involving ten older adults from the community are employed to gather knowledge and explore culturally appropriate health programs that facilitate technology usage among Indigenous older adults. Our findings underscore the importance of grounding Indigenous culture in designing ageing technology support programs for remote Indigenous communities. For instance, we found that intergenerational support, a critical component of Indigenous culture, plays a vital role in adopting ageing technology. Therefore, designing age-tech support programs without incorporating younger family members poses significant challenges within these communities.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 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".