Peace of mind: A community-industry-academic partnership to adapt dementia technology for Anishinaabe communities on Manitoulin Island
Bibliographic record
Abstract
INTRODUCTION: Aging Technologies for Indigenous Communities in Ontario (ATICON) explores the technology needs of Anishinaabe older adults in the Manitoulin region of Northern Ontario. Our program of research addresses inequitable access to supportive technologies that may allow Indigenous older adults to successfully age in place. METHODS: Using Indigenous research methodologies (IRM) and community-based participatory research (CBPR) we explored the acceptability of CareBand - a wearable location and activity monitoring device for people living with dementia using a LoRaWAN, a low-power wide-area network technology. We conducted key informant consultations and focus groups with Anishinaabe Elders, formal and informal caregivers, and health care providers (n = 29) in four geographically distinct regions. RESULTS: Overall, participants agreed that CareBand would improve caregivers' peace of mind. Our results suggest refinement of the technology is necessary to address the challenges of the rural geography and winter weather; to reconsider aesthetics; address privacy and access; and to consider the unique characteristics of Anishinaabe culture and reserve life. CONCLUSION: All three partners in this research, including the Indigenous communities, industry partner, and academic researchers, benefited from the use of CBPR and IRM. As CareBand is further developed, community input will be crucial for shaping a useful and valued device.
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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 | 0.014 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".