Exploring the impact of media and information on self-reported intentions to vaccinate against COVID-19: A qualitative interview-based study
Bibliographic record
Abstract
BACKGROUND: The World Health Organization declared vaccine hesitancy a top threat to global health following resurgence of vaccine-preventable diseases close to eradication in many countries (e.g. measles). Vaccines are effective in preventing severe illness, hospitalization, and death from COVID-19, yet there remains a small proportion of the eligible population who choose not to vaccinate. Social media and online news sources are opportunities for targeted public health interventions to improve vaccine uptake. This study reports the results of a semi-structured interview study that explored the influence of media and information on individuals' self-reported intentions to vaccinate against COVID-19. METHODS: A qualitative descriptive study was employed to gain insight from a diverse group of individuals. Adult participants were recruited through a related COVID-19 study; we used a maximum variation sampling technique and purposively sampled participants based on demographics. Interviews were conducted from February 2021 to May 2021. Themes from interviews were summarized with representative quotations according to the 3C Theoretical Framework (Confidence, Complacency, Convenience). RESULTS: Key themes identified following thematic analysis from 60 participants included: vaccine safety, choice of vaccine, fear mongering, trust in authority, belief in vaccinations (Confidence); delaying vaccination (Complacency); confusing information, access to vaccines and information (Convenience). While most participants intended to vaccinate, many expressed concerns and hesitancy. CONCLUSIONS: COVID-19 vaccine hesitancy prevents universal immunization and contradictory messages in media are a source of concern and fear. The success of future vaccine campaigns will depend upon authorities' ability to disseminate accessible, detailed, and consistent information promoting public confidence.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".