Vaccine confidence among those living with allergy during the COVID pandemic (ACCORD): A scoping review
Bibliographic record
Abstract
Background: Reports of allergic reactions to coronavirus disease 2019 (COVID-19) vaccines, coupled with an "infodemic" of misinformation, carry the potential to undermine confidence in the COVID-19 vaccines. However, no attempts have been made to comprehensively synthesize the literature on how allergic disease and fear of allergic reactions to the vaccines contribute to hesitancy. Objectives: Our aim was to review the academic and gray literature on COVID-19 vaccine hesitancy and allergic reactions. Methods: We searched 4 databases (CINAHL, PsycINFO, MEDLINE, and Embase) using a search strategy developed by content and methodologic experts. No restrictions were applied regarding COVID-19 vaccine type, country of study, or patient age. Eligible articles were restricted to 10 languages. Results: Of the 1385 unique records retrieved from our search, 60 articles (4.3%) were included. Allergic reactions to the COVID-19 vaccine were rare but slightly more common in individuals with a history of allergic disease. A fifth of the studies (13 of 60 [22%]) discussed vaccine hesitancy due to possibility of an allergic reaction. Additionally, the present review identified research on details of vaccine-related anaphylaxis (eg, a mean and median [excluding clinical trial data] of 12.4 and 5 cases per million doses, respectively) and allergic reactions (eg, a mean and median [excluding clinical trial data] of 489 and 528 cases per million doses, respectively). Conclusion: COVID-19 vaccine acceptance among individuals living with allergy and among those with no history of allergic disease may be affected by fear of an allergic reaction. Despite the low incidence of allergic reactions to the COVID-19 vaccine, fear of such reactions is one of the most commonly cited concerns reported in the literature.
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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".