Canada’s National Advisory Committee on immunization: Adaptations and challenges during the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has challenged traditional vaccine guidance infrastructure and frameworks, and added urgency and complexity to the operation of National Immunization Technical Advisory Groups (NITAGs). Canada's National Advisory Committee on Immunization (NACI) provides immunization guidance to the Public Health Agency of Canada (PHAC) who publicly shares expert and evidence-informed guidance with Canadian provinces and territories. Throughout the pandemic, NACI and PHAC implemented many adaptations to meet urgent needs for pandemic vaccine guidance. In this paper, we describe: structural adaptations in response to the accelerated pace and amount of work required to issue recommendations that were timed around product authorizations and dynamic epidemiology; technical adaptations in response to rapidly evolving evidence of variable quality which required close monitoring, and which promoted reliance on basic vaccine principles due to incomplete direct evidence; the need to provide nimble advice (e.g., off-label recommendations, preferential recommendations); communications adaptations (e.g. identify sustainable spokespeople for the committee, receive stakeholder feedback, and ensure urgent nuanced advice was communicated to a diverse audience); and research adaptations focussing on solutions to constrained supply (e.g. prioritisation, extended intervals, and heterologous schedules). The early pandemic vaccine experience has created a roadmap of lessons and adaptations that should be leveraged in future pandemic vaccine programs, and has highlighted the essential role of NITAGs to complement regulatory structures during pandemics to ensure timely, impactful, and evidence-informed public health vaccine guidance.
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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 | 0.074 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 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".