Influenza vaccination and the evolution of evidence-based recommendations for older adults: A Canadian perspective
Bibliographic record
Abstract
Older adults are at high risk from influenza and its complications, and are therefore an important population for prevention efforts. In Canada, public health efforts targeting influenza are multi-pronged and include vaccination programs as well as surveillance which informs the national surveillance reporting platform FluWatch run by the Public Health Agency of Canada. Recommendations regarding use of vaccines are made nationally by the National Advisory Committee on Immunization (NACI) and by the Comité sur l'immunisation du Québec in Quebec, while vaccination programs are planned and delivered at the provincial/territorial level as opposed to as a harmonized national immunization program. NACI performs rigorous targeted literature reviews to inform their statements, and recommendations also vary by whether they apply on Individual (pertaining to decisions for individual patients) vs. Programmatic (informing policy decisions for implementation of publicly funded vaccination programs) levels. This unique context results in inter-provincial variation in vaccine schedules and funded vaccine products. In this paper, the importance of influenza vaccination for older adults is discussed; to provide insights from the Canadian context, the evolution of NACI evidence reviews and recommendations on influenza vaccination is presented.
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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 | 0.047 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".