COVID-19 Vaccines: Barriers, Motivators, and Trusted Sources of Information for Individuals with Disabilities in the State of Alaska
Bibliographic record
Abstract
Studies on U.S. patient populations show that having an intellectual disability poses the strongest independent risk factor for having a COVID-19 diagnosis, in addition to increasing one’s likelihood of hospitalization and mortality due to COVID-19 (Gleason et al., 2021). Data show there are disparities in access to COVID-19 vaccines between adults with disabilities and those without disabilities in the U.S. (Ryerson et al., 2021). A survey was conducted to obtain information on COVID-19 vaccine potential barriers, motivators, and trusted sources of information for individuals with disabilities. The results of this survey will be used to inform vaccine distribution and education efforts in the state of Alaska. Eligible participants included individuals residing in the state of Alaska who are adults with disabilities; caregivers, guardians, and family members of individuals with disabilities; and disability service providers. Disability service providers, guardians, caregivers, and family members of individuals with disabilities cited guardian biases, lack of transportation, and inability to go on one’s own as top vaccine barriers that they perceived people with disabilities experience. Vaccinated individuals with disabilities reported they were concerned that the vaccine would worsen their medical conditions, that the vaccine could contain side effects, and that they couldn’t obtain the vaccine on their own. They said their top motivators to getting vaccinated were protecting the health of themselves, their family/friends, and their community. Individuals with disabilities indicated that their primary care providers, the CDC, and the tribal health system are their most trusted sources for information about COVID-19 vaccines, while providers perceived that family and friends, primary care providers, and elders to be individuals with disabilities’ most trusted sources about COVID-19 vaccines.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".