COVID-19 vaccine hesitancy and perceived post-vaccination adverse event: Findings from a cross-sectional survey
Bibliographic record
Abstract
INTRODUCTION: In Quebec, COVID-19 vaccine uptake among adults was high for the first two doses but decreased for the subsequent booster doses. This study assesses the relationships between attitudes towards vaccination and self-reported experience and severity of adverse events following immunization (AEFIs). METHODS: A web survey of Quebec adults who received at least one dose of the COVID-19 vaccine was conducted in September 2023. Participants share their level of vaccine hesitancy before vaccination and their experience with AEFIs after receiving a dose. Participants were asked to note the severity of the symptoms they believed were due to vaccination. Intention to receive other vaccines in the future was questioned. Two coders performed a qualitative content analysis on reported AEFIs (N = 3808). Descriptive and multivariate logistic regression analyses were performed. RESULTS: Among the 8419 vaccinated respondents, 46.7 % reported having experienced AEFIs. Fatigue or malaise (20.7 %), injection site disorder (17.3 %), musculoskeletal pain (11.2 %), headache (11.0 %), and fever (10.6 %) were the most commonly reported, respectively. Respondents who were very hesitant before the COVID-19 vaccine reported more frequently having a severe AEFI compared to those who were not hesitant (25.0 % vs 3.4 % =, p < 0.001). This affirmation stays true when all severity of adverse events are considered (68.7 % vs 36.9 %) (p < 0.001). Younger age (aOR = 0.98), being a female (aOR = 1.31), a higher education level (University degree aOR = 1.56 vs high school or less), being vaccine-hesitant in general (aOR = 1.69 vs non or less hesitant) were significantly associated (p < 0.001) with self-reported AEFIs in multivariate analysis. Self-reported AEFIs that prevented doing activities (aOR = 4.87) and being vaccine-hesitant in general (aOR = 4.94) were significantly associated with reduced intention to receive other vaccines in the future. CONCLUSION: Vaccine hesitancy could influence self-reported AEFIs and their perceived severity. Transparent and tailored communication explaining AEFIs while emphasizing strategies to mitigate these effects could helpful. Our findings also have implications for pharmacovigilance. 301 mots.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".