Environmental scan of provincial and territorial planning for COVID-19 vaccination programs in Canada
Bibliographic record
Abstract
BACKGROUND: Public health departments in Canada are currently facing the challenging task of planning and implementing coronavirus disease 2019 (COVID-19) vaccination programs. OBJECTIVE: To collect and synthesize information regarding COVID-19 vaccination program planning in each province and territory of Canada, including logistic considerations, priority groups, and vaccine safety and effectiveness monitoring. METHODS: Provincial/territorial public health leaders were interviewed via teleconference during the early planning stage of COVID-19 vaccination programs (August-October 2020) to collect information on the following topics: unique factors for COVID-19 vaccination, intention to adopt National Advisory Committee on Immunization (NACI) recommendations, priority groups for early vaccination, and vaccine safety and effectiveness monitoring. Data were grouped according to common responses and descriptive analysis was performed. RESULTS: Eighteen interviews occurred with 25 participants from 11 of 13 provinces/territories (P/Ts). Factors unique to COVID-19 vaccination included prioritizing groups for early vaccination (n=7), public perception of vaccines (n=6), and differing eligibility criteria (n=5). Almost all P/Ts (n=10) reported reliance on NACI recommendations. Long-term care residents (n=10) and healthcare workers (n=10) were most frequently prioritized for early vaccination, followed by people with chronic medical conditions (n=9) and seniors (n=8). Most P/Ts (n=9) are planning routine adverse event monitoring to assess vaccine safety. Evaluation of effectiveness was anticipated to occur within public health departments (n=3), by researchers (n=3), or based on national guidance (n=4). CONCLUSION: Plans for COVID-19 vaccination programs in the P/Ts exhibit some similarities and are largely consistent with NACI guidelines, with some discrepancies. Further research is needed to evaluate COVID-19 vaccination programs once implemented.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".