Public Engagement on Childhood Vaccination: Democratizing Policy Decision-Making Through Public Deliberation
Bibliographic record
Abstract
Abstract Immunization is considered one of the most successful and cost-effective public health interventions by the World Health Organization, preventing an estimated 2 to 3 million deaths per year globally (WHO, 2018). From a public health perspective, there is growing concern that vaccination rates are insufficient to effectively control the spread of infectious diseases. From a public trust perspective, there is increasing doubt in some groups of the claims made about vaccination by authorities. Active and meaningful public engagement in health service delivery and health research is considered essential to quality improvement of health services, greater responsiveness to public needs, and more legitimate, transparent, and accountable decision-making. Public engagement through deliberative processes has garnered increasing interest from public health researchers and policy makers on a number of health-related topics, including priority setting, planning and governance of health services, and health technology assessment (Degeling et al., 2015). Calls for deliberative approaches relating to vaccine-related policy decisions have also been made. Nevertheless, to our knowledge, there has not been any official, government-sponsored public engagement event for members of the public in Ontario to deliberate on the topic of childhood vaccination. In this chapter, we begin by providing a brief overview of the current regulatory context in Ontario with regard to childhood vaccination. We then outline the Ontario Vaccine Deliberation, an academic-led project in which a lay public was convened to discuss challenges and controversies regarding childhood vaccination in Ontario, and the recommendations that were generated and endorsed by the participants through small and large group discussions during the deliberation. We draw on the Ontario Vaccine Deliberation, as well as scholarly literature, to illustrate the importance of engaging publics in decision-making about childhood vaccination. We focus our discussion on the main issues that were raised during the deliberation, namely mandatory vaccination and non-medical exemptions, communication about vaccination, and compensation for serious adverse events following immunization. The chapter includes an appendix that examines the application of democratization processes through public deliberation to the COVID-19 pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.056 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.028 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.005 | 0.007 |
| 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".