Opportunities and Perils of Public Consultation in the Creation of COVID-19 Vaccine Priority Groups
Bibliographic record
Abstract
Abstract The management of any Public Health Emergency of International Concern (PHEIC), such as COVID-19, requires several strategies: public health surveillance and active testing of suspected cases, isolating those with the disease as well as their contacts, providing risk communication messaging for actions that people can adopt to protect themselves and their families, and distribution of available vaccines once approved. Anticipating scarcity in supply, the National Advisory Committee on Immunization, tasked with providing independent advice and recommendations on immunizations for the Public Health Agency of Canada, developed preliminary recommendations for prioritization of COVID-19 vaccines before any vaccines were even authorized for distribution in Canada. We explore in this chapter the mechanisms used to establish preliminary recommendations for COVID-19 vaccine priority groups, including different strategies for public and stakeholder engagement in those recommendations, and how three provinces made operational decisions to implement vaccine delivery within their jurisdiction. We highlight specific opportunities and challenges when the general public is engaged in evaluating prioritization recommendations, particularly when those who are consulted may not necessarily reflect the population diversity underlying equity considerations. We share public comments about COVID-19 vaccine priority groups from age-stratified (18–34 years; 35–54 years; 55 years and older) mixed-gender focus groups in Vancouver, Winnipeg, Toronto, and Ottawa, conducted in December 2020.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".