Specification of an integrated information architecture for a mobile teleoperated robot for home telecare
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to design effectively integrated information architecture for a mobile teleoperated robot in remote assistance to the delivery of home health care. METHODS: Three role classes were identified related to the deployment of a telerobot, namely, engineer, technology integrator, and health professional. Patients and natural caregivers were indirectly considered, this being a component of future field studies. Interviewing representatives of each class provided the functions, and information content and flows for each function. Interview transcripts enabled the formulation of UML (Universal Modeling Language) diagrams for feedback from participants. The proposed information architecture was validated with a use-case scenario. RESULTS: The integrated information architecture incorporates progressive design, ergonomic integration, and the home care needs from medical specialist, nursing, physiotherapy, occupational therapy, and social worker care perspectives. The integrated architecture iterative process promoted insight among participants. The use-case scenario evaluation showed the design's robustness. CONCLUSIONS: Complex innovation such as a telerobot must coherently mesh with health-care service delivery needs. The deployment of integrated information architecture bridging development, with specialist and home care applications, is necessary for home care technology innovation. It enables continuing evolution of robot and novel health information design in the same integrated architecture, while accounting for patient ecological need.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".