“They're trying to bribe you and taking away your freedoms”: COVID-19 vaccine hesitancy in communities with traditionally low vaccination rates
Bibliographic record
Abstract
Vaccination is an essential public health intervention to control the COVID-19 pandemic. A minority of Canadians, however, remain hesitant about COVID-19 vaccines, while others outright refuse them. We conducted focus groups to gauge perceptions and attitudes towards COVID-19 vaccines in people who live in a region with historically low rates of childhood vaccination. Participants discussed their perception of COVID-19 vaccines and their intention to get vaccinated, and the low rate of COVID-19 vaccine uptake in Manitoba's Southern Health Region compared to other regions in Canada. We identified three drivers of vaccine hesitancy: (1) risk perceptions about COVID-19 and the vaccines developed to protect against it, (2) religious and conservative views; and (3) distrust in government and science. Participant proposed recommendations for improving communication and uptake of the COVID-19 vaccines included: public health messages emphasising the benefits of vaccination; addressing the community's specific concerns and dispelling misinformation; highlighting vaccine safety; and emphasising vaccination as a desirable behaviour from a religious perspective. Understanding the specific anxieties elicited by COVID-19 vaccines in areas with low childhood immunization rates can inform risk communication strategies tailored to increase vaccination in these specific regions. This study adds important information on potential reasons for vaccine hesitancy in areas with historically low rates of childhood vaccination, and provides important lessons learned for future emergencies in terms of vaccine hesitancy drivers and effective risk communication to increase vaccine uptake.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".