Visualization of residents in long-term care centres through mobile natural user interfaces (NUI)
Bibliographic record
Abstract
Large volumes of formal data and informal information are generated in daily workflow activities of caregivers at long-term care centers. Health data are captured formally through record keeping using paper based forms for regular updates; however, capturing informal information related to resident activities is more challenging. This unstructured data covers social contacts, family events, therapy sessions, and other happenings. The challenges arise firstly from digitizing and aggregating these data sets, because in long-term care, both datasets are essential to assess and support well-being. Secondly, visual analytics seeks to provide caregivers with much better and more effective ways to understand changes in residents' status over long durations, while improving their services immediately. Automated processing and comparison of data is valuable yet human judgment is required to apply analyses to the care of specific residents and develop support across similar groups. This suggests that the integration of automated analysis methods and interactive visualization methods is necessary. Thirdly, direct, multi-sensor handheld devices promise a set of natural input modality in providing interaction techniques such as speech, gesture, touch, and other sensor-based techniques that may facilitate just-in-time ease of analysis. In our research, we concentrate on providing effective visual analytics tools combined with appropriate natural user interfaces (NUI). In this poster, we present a set of NUI designs towards creating a social media platform for caregivers, which integrates automated analysis methods and natural interaction techniques to enable caregivers to capture, store, visualize, and analyze both formal data and informal information. Our research will evaluate whether NUI's make a difference in supporting long-term caregivers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".