Power, ethics and normative logic related to voluntary remote monitoring used to provide independent living
Bibliographic record
Abstract
In recent years the field of gerontechnology has expanded rapidly, bringing about the development of new technologies to facilitate ageing-in-place [1][2][3][4] . Alongside this technological development, there is a need to consider the broader impacts of the application of this technology on the end user, to explore the role of technology as a data collection tool, and to identify the potential benefits of industry-academic collaboration in technology design. Content Contributing to each of these key themes, this symposium discusses findings emerging from gerontechnology research conducted in the USA and Canada. Structure There will be four oral presentations. Dr. Berridge will present results from qualitative research undertaken with older adults, healthcare professionals, and family members to examine the social and ethical considerations of using remote monitoring (RM) technologies, focussing on issues of self-determination, autonomy, privacy, and power. Dr. Sixsmith will present information on the development of an Experiential Sampling Methodology to capture mood, behaviorial, and contextual data among older adults living with bipolar disorder. Dr. Woolrych will discuss the use of video surveillance data to investigate falls among older adults in long-term care, demonstrating the potential of video data for generating in-depth understanding of how and why falls occur in long-term care and reflecting on video as a potential tool for education and knowledge transfer. Dr. Sterns will present findings from an ongoing industry/academic collaboration to develop innovative products and applications that support older adults to age-in-place, including a medication dispensing system and mobile application to monitor health status. Conclusion The symposium will highlight innovative themes emerging from gerontechnology research conducted in North America. This will be followed by discussion with symposium participants to identify opportunities and challenges from other regional contexts.
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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 | 0.053 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.085 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".