Influences, Barriers, and Facilitators to COVID-19 Vaccination: Cross-sectional Survey on Vaccine Hesitancy in 2 Rural States
Bibliographic record
Abstract
BACKGROUND: Vaccination remains one of the most effective ways to limit the spread of infectious diseases such as that caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19. Unfortunately, vaccination hesitancy continues to be a threat to national and global health. Further research is necessary to determine the modifiable and nonmodifiable factors contributing to COVID-19 vaccine hesitancy in under-resourced, underserved, and at-risk rural and urban communities. OBJECTIVE: This study aimed to identify, understand, and address modifiable barriers and factors contributing to COVID-19 vaccine hesitancy among vaccine-eligible individuals with access to the vaccine in Alaska and Idaho. METHODS: An electronic survey based on the World Health Organization (WHO) Strategic Advisory Group on Experts (SAGE) on Immunization survey tool and investigators' previous work was created and distributed in June 2021 and July 2021. To be eligible to participate in the survey, individuals had to be ≥18 years of age and reside in Alaska or Idaho. Responses were grouped into 4 mutually exclusive cohorts for data analysis and reporting based on intentions to be vaccinated. Respondent characteristics and vaccine influences between cohorts were compared using Chi-square tests and ANOVA. Descriptive statistics were also used. RESULTS: There were data from 736 usable surveys with 40 respondents who did not intend to be vaccinated, 27 unsure of their intentions, 8 who intended to be fully vaccinated with no doses received, and 661 fully vaccinated or who intended to be vaccinated with 1 dose received. There were significant differences in characteristics and influences between those who were COVID-19 vaccine-hesitant and those who had been vaccinated. Concerns related to possible side effects, enough information on long-term side effects, and enough information that is specific to the respondent's health conditions were seen in those who did not intend to be fully vaccinated and unsure about vaccination. In all cohorts except those who did not intend to be fully vaccinated, more information about how well the vaccine works was a likely facilitator to vaccination. CONCLUSIONS: These survey results from 2 rural states indicate that recognition of individual characteristics may influence vaccine choices. However, these individual characteristics represent only a starting point to delivering tailored messages that should come from trusted sources to address vaccination barriers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".