Deliberation on Childhood Vaccination in Canada: Public Input on Ethical Trade-Offs in Vaccination Policy
Bibliographic record
Abstract
BACKGROUND: Policy decisions about childhood vaccination require consideration of multiple, sometimes conflicting, public health and ethical imperatives. Examples of these decisions are whether vaccination should be mandatory and, if so, whether to allow for non-medical exemptions. In this article we argue that these policy decisions go beyond typical public health mandates and therefore require democratic input. METHODS: We report on the design, implementation, and results of a deliberative public forum convened over four days in Ontario, Canada, on the topic of childhood vaccination. RESULTS: 25 participants completed all four days of deliberation and collectively developed 20 policy recommendations on issues relating to mandatory vaccinations and exemptions, communication about vaccines and vaccination, and AEFI (adverse events following immunization) compensation and reporting. Notable recommendations include unanimous support for mandatory childhood vaccination in Ontario, the need for broad educational communication about vaccination, and the development of a no-fault compensation scheme for AEFIs. There was persistent disagreement among deliberants about the form of exemptions from vaccination (conscience, religious beliefs) that should be permissible, as well as appropriate consequences if parents do not vaccinate their children. CONCLUSIONS: We conclude that conducting deliberative democratic processes on topics that are polarizing and controversial is viable and should be further developed and implemented to support democratically legitimate and trustworthy policy about childhood vaccination.
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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 | 0.094 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.044 | 0.029 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".