A Design Iteration Towards a Multi-Modal Software System for Improving Home Care Experiences in Saskatchewan
Bibliographic record
Abstract
The COVID-19 pandemic has revealed significant challenges in the structure, delivery, and financing of long-term care (LTC) in Canada. The high number of COVID-19-related deaths among LTC residents, who are primarily older adults with multiple underlying health conditions, has raised concerns about the ability of LTC sites to effectively respond to the crisis. Consequently, in Saskatchewan, where outbreaks have occurred, there is a growing interest in expanding home care (HC) services and making investments in this area. To establish HC as a viable option for helping to address the LTC crisis, it is crucial to assess the current state of people, processes, and technologies involved in HC operations. This assessment aims to identify areas for improvement and enhance the provision of HC at various levels. This paper examines the enhancements needed for current HC processes and technologies while exploring the potential benefits of utilizing an open-data and open-source software solution to improve HC operations in Saskatchewan. Additionally, future research and development directions are discussed.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".