Analysis of COVID-19 vaccine uptake among people with underlying chronic conditions in 2022: A cross-sectional study
Bibliographic record
Abstract
Background: COVID-19 has been a global burden and vaccinations have proven to be the most effective measure to fight this pandemic. Since the approval and distribution of the vaccines, approximately 75% of District of Columbia residents have been fully vaccinated leaving a quarter of the population at risk. With the availability and approval of the booster doses to people with high-risk chronic conditions, it is important to understand the attitude of people towards vaccinations. Objective: The objective of this research study is to analyze the COVID-19 vaccination uptake among people with underlying chronic conditions residing in District of Columbia residents and to determine the reason for the hesitancy to perform targeted outreach to unvaccinated populations. Study design/methods: In 2022, we conducted a cross sectional study via a short online survey that was distributed to the target populations via email and social media. Multivariable Regression Analyses were conducted to determine the factors associated with the acceptance of the vaccination across various demographics. Results: The findings of the study demonstrate that the acceptance of COVID-19 vaccination was low among people with chronic conditions compared to those with no underlying chronic conditions, and vaccination rates strongly differ based on social determinants like education, employment, and area of residence across District of Columbia. Conclusion: The public health significance of this study is to understand the reason behind the vaccine hesitancy so that we can work towards building trust, extending outreach, creating targeted health education, and increasing access to vaccination to all communities across District of Columbia.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".