Enhancing Safety During a Pandemic Using Virtual Care Remote Monitoring Technologies and UML Modeling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: This paper describes a methodology for gathering requirements and early design of remote monitoring technology (RMT) for enhancing patient safety during pandemics using virtual care technologies. As pandemics such as COrona VIrus Disease (COVID-19) progress there is an increasing need for effective virtual care and RMT to support patient care while they are at home. METHODS: The authors describe their work in conducting literature reviews by searching PubMed.gov and the grey literature for articles, and government websites with guidelines describing the signs and symptoms of COVID-19, as well as the progression of the disease. The reviews focused on identifying gaps where RMT could be applied in novel ways and formed the basis for the subsequent modelling of use cases for applying RMT described in this paper. RESULTS: The work was conducted in the context of a new Home of the Future laboratory which has been set up at the University of Victoria. The literature review led to the development of a number of object-oriented models for deploying RMT. This modeling is being used for a number of purposes, including for education of students in health infomatics as well as testing of new use cases for RMT with industrial collaborators and projects within the smart home of the future laboratory. CONCLUSIONS: Object-oriented modeling, based on analysis of gaps in the literature, was found to be a useful approach for describing, communicating and teaching about potential new uses of RMT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it