Defining technology user needs of Indigenous older adults requiring dementia care
Bibliographic record
Abstract
00 Exploration of user needs for technology is critical for adoption and sustained use.We qualitatively examined needs for technology to enhance the wellbeing and support independent living of North American Indigenous older adults requiring dementia care.We partnered with Indigenous older adults, caregivers, health practitioners and a Community Research Advisory Committee in the File Hills Qu'Appelle Tribal Council.We sampled participants who were either coping with their own memory loss, caring for someone with dementia, or had knowledge of dementia programs and services.Interview data were gathered in three focus groups, two one-on-one interviews at a local Indigenous gathering place, and with one one-on-one telephone interview (n = 43).Many respondents recognized the value of technology for dementia care, but many responses reflected unmet needs related to technology infrastructure, with frequent mention of unstable internet access in and around Indigenous communities.Another, but related theme to lack of ICT infrastructure pertains to the general lack of computer use and low rates of exposure to ICT.Finally, data security concerns were evident with concerns voiced about ensuring safety and wellbeing of Indigenous older adults.We discuss these findings in the context of inaccessibility and argue for co-design of technology with Indigenous older adults with dementia.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".