Impact of Psycho-Social Factors, E-health Literacy and Information Access on COVID-19 Vaccination Perceptions and Intentions: Online Survey
Bibliographic record
Abstract
The COVID-19 pandemic has been associated with an infodemic which impacts on vaccination perceptions and intentions. E-health literacy seems to be the key to searching health information on the web. Age and income level impact vaccine hesitancy and resistance. It is important to know more about the population who are hesitant to get vaccinated in order to develop appropriate and accessible information. We focused on four factors that impact vaccination perceptions and intentions: socio-demographic characteristics (age and education level), e-health literacy and sources of information about COVID-19. An anonymous online survey was completed by 368 participants, who reported their age, level of education, F-eHEALS (the level of e-health literacy), preferred sources of COVID-19 information, and their vaccination perceptions and intentions (vaccine score). The vaccine score is measured by a combination of two preview questionnaires adapted to COVID-19. We first assessed our questionnaire construct on intentions and perceptions of COVID-19 vaccination. We obtained a unidimensional scale that we correlated with other factors and related to clusters (k-means). The results then showed that age, education level, and sources of COVID-19 information (radio, internet and “no channel”) impact vaccination perceptions and intentions. E-health literacy appears to be a co-variant without direct link with vaccination perceptions and intentions but linked to age and sources of COVID-19 information. This study shows how age, education level, sources of COVID-19 information and e-health literacy can impact COVID-19 vaccination perceptions and intentions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".