Vaccination in a Post-truth World: The Role of Self-rated Health, (Mis)trust, and Intuition
Bibliographic record
Abstract
This article offers a qualitative analysis of how people discursively justify and make sense of their COVID-19 vaccination intentions. Drawing on in-depth interviews with 39 people in British Columbia, Canada, just prior to the availability of a COVID-19 vaccine (Oct–Dec 2020), the objective of this study is to explore why some citizens are in favor of a COVID-19 vaccination while others are against receiving a COVID-19 vaccine. Our qualitative data reveals three key factors that inform people’s discursive justifications of their COVID-19 vaccine intention: (i) self-rated health, (ii) (mis)trust, and (iii) intuition. First, we found that vaccination justification was coordinated through participants’ self-rated views of their own health and whether they adopted an individualist or a collectivist cultural perspective of risk. Second, participants’ justification was tied to (mis)trust in government and public health initiatives, affecting participants’ upcoming willingness to receive a COVID-19 vaccination. And third, drawing on the concept of epistemic repertoires, we observed that vaccination justification was expressed through various types of intuitions that were grounded in personal “gut feelings,” religious beliefs, and scientific reasoning. Overall, our research highlights the importance of qualitatively examining the cultural and social meanings that citizens attach to vaccines and the “cultural scripts” they draw on when responding to public health vaccination initiatives. Our findings reveal the need for local, contextualized, and community generated health strategies that go beyond simply providing public health information.
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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 | 0.024 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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 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".