Manitoba Inuit Association’s Rapid Response to Include an Inuit Identifier within Manitoba COVID-19 Diagnostic Tests
Bibliographic record
Abstract
To monitor the progress of the COVID-19 outbreak, ensure equitable access to testing and treatment, and provide up-to-date information to Indigenous decision-makers engaged in setting up measures to protect their communities, the Manitoba Inuit Association (MIA) mobilized to work with the First Nation Heath and Social Secretariat of Manitoba, Ongomiizwin Research, and the Manitoba Government to identify Inuit in COVID-19 diagnostic tests, including Inuit who reside in Manitoba or those who come from Nunavut to the province to access health services. Provincial work was already underway to add Indigenous identifiers into provincial clinical health information systems; however, it was apparent early in April 2020 that reporting to Indigenous organizations on identified COVID-19 cases for First Nation, Metis, and Inuit People would be also be required in order for remedial measures to occur. This article describes the governance considerations needed to establish an information-sharing agreement with the Government of Manitoba and the role of the MIA in overseeing this process. Further background information is provided in addition to an extended discussion around the context in which Inuit are identified and receive healthcare services in Manitoba.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".