Designing portable solutions to support collaborative workflow in long-term care: A five point strategy
Bibliographic record
Abstract
Providing continuous care for residents of long-term health care facilities or for individuals requiring home care can be difficult. The daily needs of residents exist within the context of long term health goals, which are often tailored for individual residents' needs but are identified by multiple caregivers. Collaboration and clear communication between caregivers is essential for delivery of effective care. Analysis and constant sharing of resident status over time is needed in order to evaluate a status change and define treatments. By investigating the workflow in a long term health facility, we identified the key needs of caregivers to document and share resident status, analyze documentation relative to long term health goals and treatments, and to share information with other caregivers. We propose a five-point strategy for addressing these needs, derived from this investigation, as follows:, (i) data capture is supported in multiple formats, (ii) visual analytics tools are provided to analyze records, (iii) collaborative tools are provided to facilitate information sharing and the organization of care, (iv) user interaction is aided by the implementation of a natural user interface (NLH), and (v) the interface optimizes communication. In this paper, we share three prototype designs which support caregivers in a long-term care facility and are scalable for homecare use. We also present the context and the design methodology through which the designs emerged.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".