Using integrated technology to create quality care for older adults: a feasibility study
Bibliographic record
Abstract
PURPOSE: Slow changes in older adults' health status are often not detected until they escalate. Our aim was to understand if e-technology can enhance the safety and quality of older adult care by detecting changes in health status early. METHODS: E-technology was implemented with 30 seniors in an assisted living facility. We used wireless devices to monitor blood pressure, oxygen saturation, weight, and hydration. This 1-year feasibility study included: a readiness assessment, procuring devices, developing an alert software, training staff, and weekly monitoring for several months. RESULTS: Analysis of service utilization data showed no significant differences in number of emergency or hospital visits between the intervention and control group. Qualitative data suggested residents were satisfied with the e-technology. Among staff, several saw value in weekly monitoring, however staff emphasized the need for devices to be suitable for older adults. CONCLUSION: It is imperative that researchers work with facilities to ensure there is value-added in implementing new technology. Staff feedback helped fine-tune devices, training materials, and measurement process. It took longer than anticipated to procure suitable devices, set up the software, and recruit residents, thus limiting data collection. Future studies should dedicate more time to implementation and propose longer timelines.
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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 | 0.027 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".