A Shot in the Arm: How to Improve Vaccination Policy in Canada
Bibliographic record
Abstract
Recent outbreaks of measles in many parts of Canada draw attention to the importance of vaccination policy design, especially for children. Most Canadian provinces fail to meet national immunization targets for key diseases, and coverage ratios among children in a few provinces, where data are well kept and upto-date, are falling over time. If immunization coverage continues to fall, more vulnerable populations, such as children, the elderly, and people with medical conditions that may prevent them from being immunized, will be put at risk. Arguably, the general societal expectation in Canada is that people will get vaccinated, but barriers to access and the complexity of the decision mean that parents without a family physician, those in lowincome households, single parents and new arrivals in Canada are likely to not immunize or just partially immunize their children. Some parents may be active objectors to immunization, and policymakers must be careful to avoid alienating them or driving them away from the system. Most, however, appear not to immunize their children not because they actively object to vaccines, but because of barriers to access, complacency, or procrastination. Those parents are the focus of this paper, and we argue should be a focus of Canadian immunization policy. In this Commentary, we take a particularly close look at policies in Ontario, Alberta and Newfoundland and Labrador. Alberta and Ontario are relatively large provinces with different policy approaches to vaccination delivery, one focused on early interventions and the other on making immunization decisions mandatory in schools. Both models have their advantages, but neither province has reached national vaccination coverage targets. Newfoundland and Labrador has a policy design similar to Alberta’s, but some of the highest vaccination coverage in Canada. Despite the success of Newfoundland and Labrador’s vaccination policies, we do not think that there is a one-size-fits-all solution for all provinces because the characteristics of populations are different across and within provinces. That said, some basic principles of a good policy framework are explored in this paper, including the requirement for parents to make a vaccination decision, the early collection of data, access to vaccines, scope of practice, and how information is presented to new parents. We believe that well-designed vaccination policies could reach national targets while still accommodating choice. We argue that a key policy step, in provinces where needed, is to track immunization status from birth to better identify vulnerable regions in the event of an outbreak and better remind parents of the importance of immunization. Comprehensive registries at birth could help to coordinate subsequent parental reminders to immunize, and allow health officials to provide the information most relevant to parents. Further, we suggest reforms that ensure getting immunized is as easy as possible and that new parents be strongly encouraged to make a vaccination decision.
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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 | 0.019 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 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".