Successes, lessons learned and recommendations for the future: Experiences from the first year of Ontario’s nirsevimab program for respiratory syncytial virus (RSV) prophylaxis in infants
Bibliographic record
Abstract
At the conclusion of the 2024/2025 respiratory syncytial virus (RSV) season in Ontario, a panel of healthcare providers who were actively involved in the conduct of the province's first RSV prophylaxis program with nirsevimab in infants, assembled to discuss their experiences. The discussion was a follow-up to a larger pre-RSV season meeting held in June 2024 to develop recommendations supporting a universal prophylaxis program for infants in Ontario. The principal objective of the 2025 meeting was to identify components of the province's program that were successful, as well as those that posed barriers to nirsevimab uptake. A modified version of the World Health Organization's analytical framework for health systems was used to structure the discussion around five key programmatic building blocks: (1) governance and leadership, (2) funding and reimbursement, (3) logistics, demand and distribution, (4) awareness and education, and (5) surveillance and monitoring. A consensus was reached on updated recommendations to enhance the success of Ontario's nirsevimab program for future RSV seasons. These recommendations aim to ensure equitable access for all infants, achieve optimal uptake, and provide the best protection against RSV infection for Ontario's infants.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".