Describing COVID-19 immunizations for First Nations people on-reserve in Alberta using real-time integration of point of care and provincial data
Bibliographic record
Abstract
BACKGROUND: COVID-19 profoundly impacted First Nation peoples. Historically, records of on- and off-reserve vaccine delivery have been fragmented. For the first time in Canada, we aimed to describe complete immunization rates, on- and off-reserve vaccine delivery, for COVID-19 in Alberta, Canada among First Nations on-reserve. METHODS: Fifteen First Nations in Alberta, Canada participated in this prospective, descriptive cohort study whereby real-time integration (RTI) was deployed to reconcile COVID-19 vaccine delivery records on-reserve (local database) to those reported off-reserve (provincial database) between January 3, 2021-December 1, 2022. Immunization data (individuals ≥ 6 months) were aggregated into 100 one-week intervals. Weekly immunization rates were assessed by age, sex, community size, and location of vaccine administration (on- or off-reserve) using multiple linear regressions and chi2 tests. FINDINGS: 50,758 First Nation people were included, approximately 50% of whom were female. RTI data showed that 64% received at least one dose of vaccine with higher rates in older First Nation adults. No sex differences were observed. Nearly half received their first dose off-reserve and would have been missed by local public health on-reserve (local database) without the implementation of RTI. First dose immunization rates rapidly increased with graduated First Nation-specific eligibility and provincial incentives promoting uptake (p < 0.001). INTERPRETATION: We accurately assessed complete immunization rates among First Nation people receiving services on-reserve irrespective of delivery of immunizations on- or off-reserve through deployment of an innovative RTI approach. Without these RTI advances, immunization rates would have been substantially under-reported and may have misdirected public health initiatives around vaccine uptake. RTI should be a priority for all provinces in Canada to ensure accurate coverage rates for First Nation people.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".