Decisions about adopting novel COVID‐19 vaccines among White adults in a rural state, USA: A qualitative study
Bibliographic record
Abstract
PURPOSE: Many people, especially in rural areas of the United States, choose not to receive novel COVID-19 vaccinations despite public health recommendations. Understanding how people describe decisions to get vaccinated or not may help to address hesitancy. METHODS: We conducted semistructured interviews with 17 rural inhabitants of Maine, a sparsely populated state in the northeastern US, about COVID-19 vaccine decisions during the early rollout (March-May 2021). We used the framework method to compare responses, including between vaccine Adopters and Non-adopters. FINDINGS: Adopters framed COVID-19 as unequivocally dangerous, if not personally, then to other people. Describing their COVID concerns, Adopters emphasized disease morbidities. By contrast, Non-adopters never mentioned morbidities, referencing instead mortality risk, which they perceived as minimal. Instead of risks associated with the disease, Non-adopters emphasized risks associated with vaccination. Uncertainty about the vaccine development process, augmented by social media, bolstered concerns about the long-term unknown risks of vaccines. Vaccine Adopters ultimately described trusting the process, while Non-adopters expressed distrust. CONCLUSION: Many respondents framed their COVID vaccination decision by comparing the risks between the disease and the vaccine. Associating morbidity risks with COVID-19 diminishes the relevance of vaccine risks, whereas focusing on low perceived mortality risks heightens their relevance. Results could inform efforts to address COVID-19 vaccine hesitancy in the rural US and elsewhere. PATIENT OR PUBLIC CONTRIBUTION: Members of Maine rural communities were involved throughout the study. Leaders of community health groups provided feedback on the study design, were actively involved in recruitment, and reviewed findings after analysis. All data produced and used in this study were co-constructed through the participation of community members with lived experience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".