AgeTech, Ethics and Equity: Towards a Cultural Shift in AgeTech Ethical Responsibility
Bibliographic record
Abstract
Population ageing is a global phenomenon which presents major challenges for the provision of care at home and in the community (ONS, 2018). Challenges include the human and economic costs associated with increasing numbers of older people with poor physical and mental health, loneliness, and isolation challenges (Mihalopoulos et al., 2020). The global ageing population has led to a growth in the development of technology designed to improve the health, well-being, independence, and quality of life of older people across various settings (Fang, 2022). This emerging field, known as “AgeTech,” refers to “the use of advanced technologies such as information and communications technologies (ICT’s), technologies related to e-health, robotics, mobile technologies, artificial intelligence (AI), ambient systems, and pervasive computing to drive technology-based innovation to benefit older adults” (Sixsmith, et al., 2020 p1; see also Pruchno, 2019; Sixsmith, Sixsmith, Fang, and Horst, 2020). AgeTech has the potential to contribute in positive ways to the everyday life and care of older people by improving access to services and social supports, increasing safety and community inclusion; increasing independence and health, as well as reducing the impact of disability and cognitive decline for older people (Sixsmith et al, 2020). At a societal level, AgeTech can provide opportunities for entrepreneurs and businesses (where funding and appropriate models exist) (Akpan, Udoh and Adebisi, 2022), reduce the human and financial cost of care (Mihalopoulos et al., 2020), and support ageing well in the right place (Golant, 2015).
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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 | 0.058 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.111 |
| Scholarly communication | 0.028 | 0.031 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.010 | 0.025 |
| 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".