AN IOT TROLLEY FOR LONG-TERM CARE FACILITIES TO PROVIDE EFFICIENCY AND REDUCE RISKS
Bibliographic record
Abstract
Abstract One of challenges facing the long-term care facilities in Taiwan is the burden of the paperwork affecting nurses, which limits their time to look after the residents. Nurses usually estimate spending one quarter of their shifts with paperwork. The aim of this study is to develop a mobile care information system - Jubo IoT Trolley: a trolley with IoT vital-sign devices collecting and delivering timely care information to care professionals. Based on user-centered design (i.e., discover-define-develop-delivery), we conducted stakeholder interviews and rapid prototypes to zero in on the communication problem, and designed the IoT Trolley to support nurses in their daily workflow, facilitate vital-sign measurements at the bedsides, and collect the measured values to the cloud database automatically. Through design iterations, we have validated usability of the system in multiple care facilities. The result shows, with the IoT Trolley, the nurses can receive the senior’s critical vital status from the caregivers more promptly, provide instructions remotely and therefore, reduce potential care risks. Furthermore, the cloud analyzes the collected residents’ health data, the vital sign alerts can be sent to the nursing directors, so they can coordinate and intervene instantly. At last, this work demonstrates that through the technology, care qualities are improved, and care professionals can spend more valuable time with residents in the long-term care facilities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".