1768. Engaging Clinically and Socially Vulnerable Populations to Understand Their Perceptions of mRNA Vaccines
Bibliographic record
Abstract
Abstract Background One of the COVID-19 pandemic’s greatest silver linings has been the success of mRNA-based vaccines. While successful, the public has had many questions and even concerns about these vaccines as the pandemic is the first time they were clinically used. Both clinically and socially vulnerable populations have borne a hugely disproportionate burden of the pandemic. It is a public health imperative to ensure vaccine uptake in these groups are as high as possible. This project aimed to understand vulnerable populations perspectives on mRNA vaccines and immunizations to inform the development and dissemination of educational materials. Methods We conducted semi-structured qualitative descriptive interviews with clinically vulnerable (e.g., 50+, those living in LTC and congregate settings, having immunocompromising conditions/treatments) and socially vulnerable (e.g., racialized, newcomer and indigenous) populations across Canada. We thematically analyzed the data to identify patterns of meaning across the interviews. Results Fourteen people participated in interviews. Themes include: 1) clinically vulnerable populations view mRNA vaccines as safe and important for protecting themselves against COVID-19 and wish for the general public to get vaccinated to protect those who are most vulnerable; 2) socially vulnerable populations felt that there was a lack of accessible and concise information about who is high risk for COVID-19, timing of vaccination and how natural immunity to COVID-19 impacts the need for vaccination; and 3) vulnerable populations felt that the general public no longer views getting vaccinated as important since they do not view themselves as at-risk of hospitalization or severe adverse events. Conclusion These findings suggest that vulnerable populations view the vaccine as safe but perceive that getting vaccinated is not a priority for the general public. Our team is developing educational resources for vulnerable populations and the public to promote vaccine uptake. With more mRNA vaccines being developed for other infectious diseases, these findings can help inform vaccine promotion efforts and increase confidence in mRNA vaccines. Disclosures All Authors: No reported disclosures
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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".