A Human Factors Approach for Designing, Developing and Deploying Technology for Aging in the Right Place
Bibliographic record
Abstract
Technology to support aging in the right place (AIRP) has much promise, but the potential is not yet being met. In their paper outlining the opportunities and challenges in the use of technology to support AIRP, Kokorelias et al. (2024) provided a roadmap for the next steps. Our commentary focuses on two questions they raised: (1) How can technology be designed and developed to better meet the specific needs, preferences and abilities of older adults? (2) How do we evaluate technology in natural settings? Widespread technology adoption will emerge from consideration of the users; an understanding of their unique needs; iterative participatory design and user testing; and support for facilitating conditions to ease deployment into people's lives and minimize abandonment.
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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 | 0.068 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.014 | 0.061 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".