“<i>If It’s My Time”</i>: A Qualitative Study of COVID-19 Vaccine Intention Among a Sample of Arab Americans
Bibliographic record
Abstract
Little is known about vaccine willingness in Arab Americans. It stands to reason that factors such as increased risks of experiencing xenophobia and discrimination and limited social support, particularly among new immigrants, may influence COVID-19 vaccine willingness among Arab Americans. We qualitatively investigate the psychological, social, and physical impacts of the COVID-19 pandemic on Arab Americans and explore how these experiences may have influenced COVID-19 vaccine perceptions and behaviors. We conducted a qualitative study following an interpretivist, inductive paradigm among a subset of Arab Americans (N=23) living in the US between April and July 2021. We identified four broad categories of themes: individual factors contributing to COVID-19 vaccine willingness, perceptions of the US government and the public health response, the impact of media on the COVID-19 pandemic and perceptions about the COVID-19 vaccine, and perceived COVID-19 severity. COVID-19 vaccine willingness was based on participants’ perception of the severity of the COVID-19 pandemic, protecting their health and that of others in their social circle, a work or school requirement, or fulfilling a greater social responsibility. Though our study disproportionately represented those who were vaccine-willing, participants referenced stories about people in their immediate and distal networks who were unwilling to be vaccinated. There are complex connections between individual well-being, community identity and belonging, and health for Arab Americans that deserve additional attention.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".