COVID-19 Vaccination Among US-Born and Non–US-Born Residents of the United States From a Nationally Distributed Survey: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Extended literature has demonstrated that COVID-19 vaccination is crucial for the health of all individuals, regardless of age. Research on vaccination status in the United States (US) among US-born and non-US-born residents is limited. OBJECTIVE: The objective of our study was to examine COVID-19 vaccination during the pandemic among US-born and non-US-born people, while accounting for sociodemographic and socioeconomic factors gathered through a nationally distributed survey. METHODS: A descriptive analysis was conducted on a comprehensive 116-item survey distributed between May 2021 and January 2022 across the US by self-reported COVID-19 vaccination and US/non-US birth status. For participants that responded that they were not vaccinated, we asked if they were "not at all likely," "slightly to moderately likely," or "very to extremely likely" to be vaccinated. Race and ethnicity were categorized as White, Black or African American, Asian, American Indian or Alaskan Native, Hawaiian or Pacific Islander, African, Middle Eastern, and multiracial or multiethnic. Additional sociodemographic and socioeconomic variables included gender, sexual orientation, age group, annual household income, educational attainment, and employment status. RESULTS: The majority of the sample, regardless of whether they were US-born or non-US-born, reported being vaccinated (3639/5404, 67.34%). The US-born participants with the highest proportion of COVID-19 vaccination self-identified as White (1431/2753, 51.98%), while the highest proportion of vaccination among non-US-born participants was found among participants who self-identified as Hispanic/Latino (310/886, 34.99%). Comparing US-born and non-US-born participants showed that among those who were not vaccinated, the highest self-reported sociodemographic characteristics by proportion were similar between the groups, and included identifying as a woman, being straight or heterosexual, being aged 18 to 35 years, having an annual household income <$25,000, and being unemployed or taking part in nontraditional work. Among the 32.66% (1765/5404) of participants that reported not being vaccinated, 45.16% (797/1765) stated that they were not at all likely to seek vaccination. Examining US/non-US birth status and the likelihood to be vaccinated for COVID-19 among nonvaccinated participants revealed that the highest proportions of both US-born and non-US-born participants reported being not at all likely to seek vaccination. Non-US-born participants, however, were almost proportionally distributed in their likelihood to seek vaccination; they reported to be "very to extremely likely" to vaccinate (112/356, 31.46%); compared to 19.45% (274/1409) of US-born individuals reporting the same. CONCLUSIONS: Our study highlights the need to further explore factors that can increase the likelihood of seeking vaccination among underrepresented and hard-to-reach populations, with a particular focus on tailoring interventions for US-born individuals. For instance, non-US-born individuals were most likely to vaccinate when reporting COVID-19 nonvaccination than US-born individuals. These findings will aid in identifying points of intervention for vaccine hesitancy and promoting vaccine adoption during current and future pandemics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".