Engaging Indigenous older adults with technology use to respond to health and well-being concerns and needs
Bibliographic record
Abstract
Increased access to technology can promote independent living, stimulate cognitive functioning, relieve caregiver stress, improve telehealth access, increase overall well-being, and be used to share cultural resources such as Indigenous language applications. Many Indigenous older adults would like to learn more about technology and recognize the value of technology in supporting healthy ageing; however, as Morning Star Lodge has previously determined, accessibility and readiness were key factors in the use of this technology. Utilizing the guiding principles of the Model of Engaging Communities Collaboratively and the Ethical Engagement Training Module, Morning Star Lodge partnered with the Star Blanket Cree Nation to support the healthy lifestyle of six Indigenous older adults by increasing their access to and engagement with culturally safe technology solutions individual to their specific health and lifestyle needs. These co-researchers were provided with tablets, MiFis (mobile internet access), and learning workshops and were interviewed pre- and post-workshops to assess their comfort level with the device and information received. Additionally, these interviews assessed how the technology helped to address the health needs of the co-researchers. The findings demonstrated that the technology met the health needs of the older adults, particularly with the emergence of the COVID-19 pandemic and the need to stay connected to loved ones. The information gained through this work will support public health workers in responding to the needs of older Indigenous adults using technology to meet their health and well-being. There is also a significant need for pandemic preparedness work to be done with Indigenous communities and this work could inform this in part.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".