Assessing barriers to access and equity for COVID-19 vaccination in the US
Bibliographic record
Abstract
BACKGROUND: Historical vaccination coverage in economically disadvantaged, ethnic minority, non-affluent white and agricultural populations in the US has lagged coverage in more affluent urban and suburban white populations due to a variety of social and economic factors. In the current COVID-19 pandemic, sociocultural and economic challenges continue to present significant obstacles to achieving equitable uptake of COVID-19 vaccines. The goal of this study was to qualitatively assess perceptions of key US healthcare stakeholders of the most significant barriers to COVID-19 vaccine access and equity to better characterize their expected impact on US communities. METHODS: After conducting a targeted literature review (TLR), we hypothesized 20 high-impact barriers which included structural and logistical barriers, capturing systemic challenges to vaccine accessibility, and attitudinal and informational barriers, affecting patient willingness to pursue vaccination. We developed a qualitative discussion guide, which included both open-ended and closed-ended questions, and interview stimulus material to conduct one-on-one in-depth interviews to assess the expected prevalence, severity, and persistence of these 20 high-impact barriers, which were hypothesized based on TLR. As a part of this qualitative study, we conducted one-on-one in-depth interviews with a diverse set of 15 US healthcare stakeholders who were involved in the COVID-19 vaccine rollout in states with relatively disparate vaccination rates by ethnicity. These stakeholders were selected to reflect an array of roles in the COVID-19 vaccine rollout, including infectious disease specialists, pharmacists, community advocacy representatives, and partners of local governments involved in the COVID-19 vaccine rollout and community education. RESULTS: Respondents identified limited vaccination sites in rural settings and technology-related barriers as the most prevalent and severe structural and logistical barriers in US communities. Respondents assessed COVID-19 vaccine safety concerns and politically motivated skepticism to be the most prevalent and severe attitudinal and informational barriers. Respondents cited proliferation of mobile vaccination clinics and local community messaging to endorse vaccines as the most effective solutions to these top structural and attitudinal barriers. Respondents expected politically motivated skepticism to be the most significant and persistent barrier to broader vaccine uptake in the US. CONCLUSIONS: Our study suggests that attitudinal barriers, particularly politically motivated skepticism, are likely to remain the most persistent challenges to widespread vaccination against COVID-19 in the US.
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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 | 0.009 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".