Bibliographic record
Abstract
Purpose This paper aims to provide an overview of the emerging AgeTech sector and highlight key areas for research and development that have emerged under COVID-19, as well as some of the challenges to real-world implementation. Design/methodology/approach The paper is a commentary on emerging issues in the AgeTech sector, with particular reference to COVID-19. Information used in this paper is drawn from the Canadian AGE-WELL network. Findings The COVID-19 pandemic has particularly impacted older adults. Technology has increasingly been seen as a solution to support older adults during this time. AgeTech refers to the use of existing and emerging advanced technologies, such as digital media, information and communication technologies (ICTs), mobile technologies, wearables and smart home systems, to help keep older adults connected and to deliver health and community services. Research limitations/implications Despite the potential of AgeTech, key challenges remain such as structural barriers to larger-scale implementation, the need to focus on quality of service rather than crisis management and addressing the digital divide. Practical implications AgeTech helps older adults to stay healthy and active, increases their safety and security, supports independent living and reduces isolation. In particular, technology can support older adults and caregivers in their own homes and communities and meet the desire of most older adults to age in place. Social implications AgeTech is helpful in assisting older adults to stay connected. The COVID-19 pandemic has shown the importance of the informal social connections and supports within families, communities and voluntary organizations. Originality/value The last months have seen a huge upsurge in COVID-19-related research and development, as funding organizations, research institutions and companies pivot to meet the challenges thrown up by the pandemic. This paper looks at the potential role of technology to support older adults and caregivers.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.055 | 0.008 |
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".