Optimizing the acceptability, effectiveness and costs of immunization programs: the Quebec experience
Bibliographic record
Abstract
In Canada, publicly funded immunization programs are a provincial/territorial responsibility. In the province of Quebec, much effort has been devoted to optimize the acceptability, effectiveness and cost-effectiveness of publicly funded immunization programs for children during the last 20 years. The aim of this article is to describe how programs are planned, implemented and evaluated and to identify key factors that contribute to the success of this enterprise. A comprehensive framework was developed for the evaluation of new vaccines and new programs in a societal perspective. It is used by the Quebec Immunization Committee to prepare reports proposing options with their costs and consequences for the public health authority. When a decision is made, the implementation of the new program is carefully planned. Surveys and consultations with stakeholders are systematically conducted to identify potential obstacles. A fraction of the budget is always reserved for program evaluation and monitoring. At the present time, the recommended immunization schedule targets 15 different diseases and only 20 injections are offered up to 15 years of age. Vaccine uptake rate is high and, although a reduced number of doses are recommended for several vaccines, program effectiveness is highly satisfactory, as shown for hepatitis B, meningococcal and pneumococcal diseases.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".