Ageing, technology, and health: Advancing the concepts of autonomy and independence
Bibliographic record
Abstract
The global pandemic expedited the adoption of AgeTech solutions that aim to help older adults maintain their autonomy and independence. This article examines the negative impact of the Western worldview of autonomy and independence on older adults. Negative impact can manifest as ageism and may be compounded by intersections of identities with race, gender, and culture. We propose an inclusive framework for health leaders, one that is not binary or categorical, but instead, on a continuum: (1) relational autonomy which assumes that relationships form one's identity; therefore, no one is autonomous to the exclusion of others, and (2) interdependence which proposes that one's lifestyle choice is supported by interreliance with aspects of one's environment. We examine two examples of AgeTech from the perspective of relational autonomy and interdependence and discuss how health leaders can use this inclusive framework to ensure that their services do not discriminate against older adults.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".