Factors Associated With COVID-19 Vaccine Acceptance Among Patients Receiving Care at a Federally Qualified Health Center
Bibliographic record
Abstract
Background:COVID-19 vaccine hesitancy in the United States is high, with at least 63 million unvaccinated individuals to date. Socioeconomically disadvantaged populations experience lower COVID-19 vaccination rates despite facing a disproportionate COVID-19 burden.Objective:To assess the factors associated with COVID-19 vaccine acceptance among under-resourced, adult patients.Methods:Participants were patients receiving care at a Federally Qualified Health Center (FQHC) in St. Paul, Minnesota. Data were collected via multiple modes over 2 phases in 2020 (self-administered electronic survey) and 2021 (study team-administered survey by telephone, self-administered written survey) to promote diversity and inclusion for study participation. The primary outcome was COVID-19 vaccine acceptance. Using logistic regression analysis, associations between vaccine acceptance and factors including risk perception, concerns about the COVID-19 vaccine, social determinants of health (SDOH), co-morbidities, pandemic-induced hardships, and stress were assessed by adjusted odds ratios (AORs) and 95% confidence intervals (CI).Results:One hundred sixty-eight patients (62.5% female; mean age [SD]: 49.9 [17.4] years; 32% <$20 000 annual household income; 69% P < .001), while concerns about the vaccine (eg, safety, side effects, rapid development of the vaccine, etc.) were negatively associated with vaccine acceptance (all P < .001). SDOH, co-morbidities, pandemic-induced hardships were not associated with vaccine acceptance.Conclusions:Our study in a socioeconomically disadvantaged population suggests that risk perception is associated with an increased likelihood of vaccine acceptance, while concerns about the COVID-19 vaccine are associated with a lower likelihood of vaccine acceptance. As these factors could impact vaccine uptake, consistent, innovative, and context-specific risk communication strategies may improve vaccine coverage in this population.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".