Communicating about COVID-19 Vaccines: A Qualitative Study on Preferences, attitudes, and Influences on Canadian Post-secondary Student Decisions to Get Vaccinated
Bibliographic record
Abstract
Given the reasonably low COVID-19 vaccine rates (e.g., <60% fully vaccinated) among Canadian young adults (18–29), we sought to understand the factors that motivated this demographic to get vaccinated. Fifteen post-secondary students and graduates participated in semi-structured interviews about the type of health information they received during the pandemic, and their communication preferences. Analysis of interview data revealed four themes: (1) Participants had high science and health literacy which equipped them to identify and dismiss misinformation; (2) Participants expressed high trust in official sources which positively impacted their confidence in the vaccines; (3) Participants exhibited a low perceived risk from COVID-19 infection and got vaccinated for reasons beyond personal protection; and, (4) Participants responded best to communication that was targeted and tailored towards them. These themes form a foundation for effective vaccine communication campaigns targeted and tailored toward young adults, which is essential for acceptance of future vaccine recommendations.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".