Bibliographic record
Abstract
Vaccination is considered to be one of the greatest public health achievements, contributing to a substantial decline in infectious disease mortality in Canada. However, a growing threat of vaccine hesitancy has led to an upsurge in the prevalence and incidence of vaccine-preventable diseases across the globe, including Canada. Vaccine hesitancy is on the rise in the province of Ontario. Parental vaccine hesitancy, vaccine misconceptions, rising non-medical vaccine exemption rates, and low childhood vaccination coverage has led to a resurgence in vaccine-preventable diseases, especially measles. Given the importance of achieving high vaccine coverage to avoid vaccine-preventable diseases and their dire consequences, vaccine hesitancy is an important issue that needs to be addressed. There is no perfect solution to address vaccine hesitancy. Understanding the complex mix of factors that determine individual and collective vaccination behaviour is vital to designing effective vaccination policies, programs, and targeted interventions. This article critiques current vaccine policy strategies and outlines a policy approach to address parental vaccine hesitancy and prevent future vaccine-preventable disease outbreaks, specifically in Ontario, and more broadly within Canada. Providing support to healthcare providers and primary care physicians; and empowering parents, schools, students, families, and communities in Ontario, will slowly but surely mitigate vaccine hesitancy and enable healthy vaccination behaviours. Healthcare system-based interventions seem to be the most comprehensive approach that requires coordinated efforts and partnerships between community-based organizations and vaccination providers to ensure inclusive and integrated service delivery.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".