Tech-Enabled Aging in the Right Place Will Only Succeed by Harmonizing Innovation With the Provision of Person-Centred Care
Bibliographic record
Abstract
The evolving concept of "[a]geing in the right place (AIRP)" (Iciaszczyk et al. 2022: 1) underscores the importance of enabling older adults to receive comprehensive care and support across various settings. There is growing evidence that innovative technologies can empower more persons to maintain their autonomy while better ensuring their safety, well-being and quality of life and also improve the experience of family caregivers and paid care providers. While there exists a powerful belief that technologies can solve all problems, the reality is that they can also present risks, particularly around cybersecurity, privacy and ethical concerns and not deliver any real benefits and in some cases, cause users harm. This paper summarizes a number of pragmatic strategies for addressing these challenges and maximizing the impact of technology in supporting AIRP.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".