Frail or not? \nAn explorative mixed methods evaluation of \na sensory-based frailty assessment toolkit
Bibliographic record
Abstract
The worlds population is aging (United Nations, 2015). As the aging population is more prone to\ndeveloping frailty, it is important to assess and monitor this condition. Frailty is a state of health where\nones overall well-being and ability to function independently are reduced, with an increased vulnerability\nto deterioration (Morley et al., 2003). Current assessment methods of frailty are prone to error due to\nhuman bias and memory loss and rely on well-trained clinicians to interpret results. Frailty is a dynamic\ncondition and continuous assessment would assist in diagnosing the condition early on. A prototype of a\nfrailty toolkit is being developed by Chao Bian and his team at the IATSL in Toronto to monitor and assess\nfrailty in older adults’ homes. This toolkit will assess frailty by measuring Fried’s Frailty Phenotypes (Fried\net al., 2001) through home monitoring technologies. It is important to involve older adults in the\ndevelopment of this toolkit as research shows that lack of user involvement is a reason for assistive\ntechnology abandonment. This study therefore researched older adults attitudes and preferences\ntowards home monitoring technologies. A focus group study was carried out, which provided insights on\nwhat technologies older adults want to interact with and what issues were perceived with in-home frailty\nmonitoring. Privacy proved to be a concern for most older adults, corresponding with previous research\n(Courtney et al., 2008). The data from the focus group was used to select technologies for the toolkit. This\ntoolkit was evaluated with an online usability assessment. Results show that the toolkit in general was\nwell received. However, participants indicated points of improvement such as the ability to personalize\nwhen users are prompted to interact with the toolkit. The results also suggested that proper explanation\nis needed to address why the toolkit is necessary for older adults and their clinicians.
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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 | 0.056 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".