Attitudes and Beliefs of COVID-19 and Vaccine Uptake among Amish Women
Bibliographic record
Abstract
The Amish, a Christian religious group living in rural areas with distinct beliefs about remaining separate from the outside world, have communities in 31 states and four Canadian provinces with just over 600 settlements. Their access to health care and technology is often limited. Several studies have noted low vaccination rates for preventable diseases among the Amish, often due to lack of knowledge about efficacy and safety of vaccines. To gain an understanding of beliefs surrounding COVID-19 and attitudes toward vaccine uptake, we surveyed 863 Amish and Mennonite women throughout Ohio who participated in rural mobile health clinics between 2015 and 2019 at two time periods: before and after the 2020 election. We received 372 completed surveys, 252 of which were completed by respondents who identified themselves as Amish. While 100% of the Amish respondents had heard of COVID-19 and 90% reported knowing someone who had contracted the disease, a mere 1.7% (4) indicated a willingness to get vaccinated. In terms of COVID-19 diagnosis, post-election participants were two times more likely to report having a positive test than pre-election respondents (p = .011). Qualitative analyses revealed significant differences in keywords used to describe COVID-19. Post-election respondents were less likely to use words like "evil" and "bad" and associate COVID-19 with the flu. A notable shift in vaccine hesitancy among Amish participants centered on the perceived politicization of the pandemic and safety/efficacy of the vaccines. Public health efforts should center on raising awareness of the severity of COVID-19 and the benefits of vaccine uptake for distinctive subcultures like the Amish.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".