Considering the impact of vaccine communication in the COVID-19 pandemic among adults in Canada: A qualitative study of lessons learned for future vaccine campaigns
Bibliographic record
Abstract
We aimed to understand how experiences with vaccine-related information and communication challenges during the COVID-19 pandemic impacted motivations and behaviors among Canadian adults regarding future vaccines. Semi-structured interviews were conducted with participants purposively selected to ensure diversity in age, sex at birth, self-identified gender, and region. Data were analyzed using thematic analysis; findings were mapped to the Information-Motivation-Behavioral Skills Model focusing on factors affecting vaccine hesitancy and uptake. Of 62 interviews completed, most were with woman (n = 32, 51.6%) and residents of Ontario (n = 36, 58.1%); the median age was 43.5 yr (interquartile range 23.3 yr). Themes included: 1) accessibility of information, 2) ability to assess information accuracy and validity, 3) trust in communications from practitioners and decision-makers, and 4) information seeking behaviors. Participants expressed various concerns about vaccines, including fears about potential side effects, particularly regarding the long-term effects of novel vaccinations. These concerns may reflect broader societal anxieties, which have been intensified by widespread misinformation and an overload of vaccine information. Moreover, participants highlighted a lack of trust in the information provided by government agencies and pharmaceutical companies, primarily driven by concerns regarding their underlying motives. Concerns about COVID-19 vaccine safety and effectiveness negatively impacted future vaccine attitudes and behaviors. Vaccine hesitancy studies should consider how individuals receive, perceive, and seek information within social contexts and risk profiles.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".