Feature Story: U of R researcher awarded more than $500,000 to develop long-term digital health solution for Indigenous communities
Bibliographic record
Abstract
During the first wave of the COVID-19 pandemic, northern Indigenous communities in Saskatchewan experienced a rapid surge of COVID-19 cases while the rest of the province showed signs of recovery. This was a stark indication that outbreaks in remote Indigenous communities differed from larger population centres, revealing the urgent need for health innovations in these communities. With the support of the federal government, through the Canadian Institutes of Health Research (CIHR) Project Grant, Dr. Tarun Katapally has been awarded $554,434 over three years for just such an innovation. His project, CO-Away: Implementation and Evaluation of Digital Health Solutions for Indigenous Self-Determination, Governance, and Data Sovereignty, is a digital health platform that will allow for data-driven, rapid responses to health crises.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.066 | 0.021 |
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".