Diagnostic testing and vaccination for COVID-19 among First Nations, Metis and Inuit in Manitoba, Canada: protocol for a nations-based cohort study using linked administrative data
Bibliographic record
Abstract
INTRODUCTION: Decades of research demonstrate that First Nations, Metis and Inuit (FN/M/I) populations have differential access to diagnostic and therapeutic healthcare. Emerging evidence shows that this continues to be the case during the SARS-CoV-2 pandemic. In an effort to rectify these differences in access to care, our team, which is co-led by FN/M/I partners, will generate and distribute evidence on COVID-19 diagnostic testing and vaccination in high-priority FN/M/I populations in Manitoba, with the goal of identifying system-level and individual-level factors that act as barriers to equitable care and thereby informing Indigenous-led public health responses. METHODS AND ANALYSIS: Our nations-based approach focuses on FN/M/I populations with separate study arms for each group. Linked administrative health data on COVID-19 diagnostic testing and vaccinations are available on a weekly basis. We will conduct surveillance to monitor trends in testing and vaccination among each FN/M/I population and all other Manitobans, map the geographic distribution of these outcomes by health region and tribal council, and identify barriers to testing and vaccination to inform public health strategies. We will follow the course of the pandemic starting from January 2020 and report findings quarterly. ETHICS AND DISSEMINATION: Ethics approvals have been granted by the University of Manitoba Research Ethics Board and from each of our FN/M/I partners' organisations. Our team is committed to engaging in authentic relationship-based research that follows First Nations, Metis and Inuit research ethics principles. Our FN/M/I partners will direct the dissemination of new information to leadership in their communities (health directors, community health organisations) and to decision-makers in the provincial Ministry of Health. We will also publish in open-access journals. The study will create ongoing capacity to monitor Manitoba's pandemic response and ensure potential health inequities are minimised, with learnings applicable to other jurisdictions where detailed administrative data may not be available.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
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 | 0.038 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".