Exploring Misconceptions Surrounding Vaccinations: A Health Advocacy Project
Bibliographic record
Abstract
Despite a consistent effort by the scientific community to inform individuals about the efficacy and safety of vaccinations, harmful misconceptions still persist which have prevented their widespread acceptance within the general public. According to the most recent survey from the Public Health Agency of Canada (2017), childhood vaccination rates (children aged two years or less) were approximately 70-90% depending on the type of vaccine, and in each province, were below the national goal1. Additionally, the Canadian childhood vaccination rates reported for the common vaccines diphtheria, tetanus, pertussis, and measles were reported to be some of the lowest in the developed world2. T o better understand these disparities, this video explores common misconceptions individuals may have toward vaccines. Specifically, this project takes the form of a skit which explores the perspectives of a female high school student, a mother of young children, and a male university student to understand their skepticism towards the HPV, MMR, and flu vaccines respectively. Overall, the aim of this project is to better inform the public about the benefits of vaccines in order to increase vaccination rates in Canada and improve health care outcomes for infectious diseases that are preventable by vaccines.
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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 | 0.037 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.010 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".