Among sheeples and antivaxxers: Social media responses to COVID-19 vaccine news posted by Canadian news organizations, and recommendations to counter vaccine hesitancy
Bibliographic record
Abstract
BACKGROUND: To create a successful public health initiative that counters vaccine hesitancy and promotes vaccine acceptance, it is essential to gain a strong understanding of the beliefs, attitudes and subjective risk perceptions of the population. METHODS: A qualitative analysis of coronavirus disease 2019 (COVID-19) vaccine discourse from 3,731 social media posts on the Twitter and Facebook accounts of six Canadian news organizations was used to identify the perceptions, attitudes, beliefs and intentions of Canadian news organizations' social media commenters toward taking a COVID-19 vaccine. RESULTS: Four main themes were identified: 1) COVID-19 vaccine safety and efficacy concerns; 2) conspiracy theories stemming from mistrust in government and other organizations; 3) a COVID-19 vaccine is unnecessary because the virus is not dangerous; and 4) trust in COVID-19 vaccines as a safe solution. Based on themes and subthemes, several key communication recommendations were developed for promotion of COVID-19 vaccine acceptance, including infographics championed by Public Health that highlight the benefits of the vaccine for those who have received it, public education about the contents and safety of the vaccine and eliciting an emotional connection through personal stories of those impacted by COVID-19. CONCLUSION: Specific considerations, such as leveraging the public's trust in healthcare professionals to act as a liaison between Public Health and the Canadian public to communicate the benefits of the vaccine against COVID-19 and its variants, may help reduce COVID-19 vaccine hesitancy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".