Royal society of Canada COVID-19 report: Enhancing COVID-19 vaccine acceptance in Canada
Bibliographic record
Abstract
COVID-19 vaccine acceptance exists on a continuum from a minority who strongly oppose vaccination, to the “moveable middle” heterogeneous group with varying uncertainty levels about acceptance or hesitancy, to the majority who state willingness to be vaccinated. Intention for vaccine acceptance varies over time. COVID-19 vaccination decisions are influenced by many factors including knowledge, attitudes, and beliefs; social networks; communication environment; COVID-19 community rate; cultural and religious influences; ease of access; and the organization of health and community services and policies. Reflecting vaccine acceptance complexity, the Royal Society of Canada Working Group on COVID-19 Vaccine Acceptance developed a framework with four major factor domains that influence vaccine acceptance (people, communities, health care workers; immunization knowledge; health care and public health systems including federal/provincial/territorial/indigenous factors)—each influencing the others and all influenced by education, infection control, extent of collaborations, and communications about COVID-19 immunization. The Working Group then developed 37 interrelated recommendations to support COVID vaccine acceptance nested under four categories of responsibility: 1. People and Communities, 2. Health Care Workers, 3. Health Care System and Local Public Health Units, and 4. Federal/Provincial/Territorial/Indigenous. To optimize outcomes, all must be engaged to ensure co-development and broad ownership.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".