The balance of risks and benefits in the COVID-19 “vaccine hesitancy” literature: An umbrella review
Bibliographic record
Abstract
Background: “Vaccine hesitancy” (VH) has been described as a “threat to global health”, especially in the COVID-19 era. Research on VH indicates that the concerns of vaccine recipients with the balance of risks and benefits of COVID-19 vaccination, which involve safety and effectiveness considerations (hereafter “safety concerns”), are a leading driver of VH. However, what explains these concerns is underexplored. Goal: We conducted a qualitative umbrella review following PRISMA guidelines and informed by a critical perspective to examine how the safety concerns of COVID-19 vaccine recipients are addressed in the VH literature. Methods: We searched PubMed, the Epistemonikos COVID-19 platform (COVID-19 L. OVE), and the WHO Global Research on COVID-19 Database. We included 49 refereed reviews examining VH in any population involved with COVID-19 vaccination decisions for themselves or as caretakers, with no methodological, quality, temporal, or geographic restrictions, and were published in English, excluding those that authors did not identify as “systematic”. Two reviewers completed article screening and data extraction and synthesis. Thematic synthesis was used to identify themes and frequencies were calculated to assess the strength of support for themes. Disagreements were resolved through full team discussion. The protocol was registered with PROSPERO (ID CRD42022351489) and partially funded by a SSHRC grant (# 435-2022-0959).Findings: All reviews assumed that VH was a major barrier to ending the COVID-19 crisis. With vaccines assumed to be “safe and effective”, recipients’ safety concerns were downplayed. Evidence incompatible with “VH-as-a-problem”, whenever mentioned, was dismissed as “misinformation”. Informed consent was either not discussed or was presented as a potential threat to “vaccine confidence”. We observed no differences regardless of study population, methodology, or other study characteristics. Limitations are discussed. Conclusions: Neglecting or dismissing vaccine recipients’ safety concerns contributes to the problem that research on COVID-19 VH purports to address. It also undermines the implementation of informed consent, critical to ethical medical and public health research, policy, and practice. The scant attention to bioethical considerations in current COVID-19 VH research is concerning. PUBLICATION AVAILABLE @: https://researchandappliedmedicine.com/revistas/vol2/revista1/umbrella-ingles.pdf
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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 | 0.046 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.023 | 0.018 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".