EFFECTS OF IMPLEMENTING AGETECH TO SUPPORT DEMENTIA CARE IN A RURAL AND NORTHERN SETTING
Bibliographic record
Abstract
Abstract Older adults wishing to age in northern and rural communities deserve equitable access to technologies that support optimal health, well-being, and quality of life. A new dementia care home opened in a rural and northern community in 2022 with consciously embedded multiple AgeTech solutions to enhance residents quality of life and care. The AgeTech solutions including hydroponic gardening, circadian lighting, and virtual exercise programming, were implementated into the 10-bed facility through a partnership between the Center for Technology Adoption for Aging in the North (CTAAN), health systems leaders, and community partners. The purposeful implementation of AgeTech within the facility through this partnership aimed to enhance the life-skills and living model of care to maximize independence, function, and resident well-being. We conducted a process evaluation assessing data from interviews with facility staff, health systems leaders, and secondary analysis of existing documentation to objectively measure the implementation process and inform a framework for ongoing assessment of AgeTech. Results highlight areas for ongoing technology innovation and inform policy decisions for future initiatives. Informants described the benefits in use of non-invasive circadian lighting technology to support resident well-being by enhancing sleep, rest/activity, reducing behaviours associated with dementia. Technology enhanced environmental well-being and expanded opportunities for residents to engage in meaningful activities. Participants also described the physical and social benefits for residents using individual and social multiplayer modes of exercise technologies. Providing equitable access to services across dispersed and sparse populations is challenging, yet with implementation of AgeTech quality of care can be enhanced.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".