Mistrust of the medical profession and higher disgust sensitivity predict parental vaccine hesitancy
Bibliographic record
Abstract
Despite overwhelming evidence that vaccines are safe and effective, there has been a rise in vaccine hesitancy and refusal leading to increases in the incidence of communicable diseases. Importantly, providing scientific information about the benefits of vaccines has not been effective in counteracting anti-vaccination beliefs. Considering this, better identification of those likely to be vaccine hesitant and the underlying attitudes that predict these beliefs are needed to develop more effective strategies to combat anti-vaccination movements. Focusing on parents as the key decision makers in their children's vaccination, the aim of this study is to better understand the demographic and attitudinal predictors of parental vaccine hesitancy. We recruited 484 parents using Amazon MTurk and queried their attitudes on childhood vaccination, level of education, age, religiosity, political affiliation, trust in medicine, and disgust sensitivity. We found three main demographic predictors for parental vaccine hesitancy: younger age, lower levels of education, and greater religiosity. We also found vaccine hesitant parents to have significantly less trust in physicians and greater disgust sensitivity. These results provide a clearer picture of vaccine hesitant parents and suggest future directions for more targeted research and public health messaging.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".