The Risk-Benefit Balance in the COVID-19 “Vaccine Hesitancy” Literature: An Umbrella Review Protocol
Bibliographic record
Abstract
Background: “Vaccine hesitancy” has been described as a major public health problem, especially in the COVID-19 era. Identified factors driving “hesitancy” include the concerns of recipients with the safety, side effects, and risk-benefit ratio of COVID-19 vaccines[1] — a proper assessment and disclosure of which are critical to the requisite process of informed consent. However, the expert literature has given little attention to the evidence informing these concerns, focusing instead on features of the recipients themselves to explain the phenomenon of so-called “hesitancy”. Goal: This umbrella review will expand the scope of research on “vaccine hesitancy” by examining how the safety, side effects, and risk-benefit ratio concerns of recipients of COVID-19 vaccines are addressed in the expert literature. Inclusion criteria: We will include systematic reviews on COVID-19 “vaccine hesitancy” that examine hesitancy in any population involved with COVID-19 vaccination decisions for themselves or as caretakers (e.g., decisions about “vaccinating” their children) to capture the broadest possible range of perspectives on the phenomenon of interest. Only completed, published, and refereed systematic reviews in English will be included. Methods: We will search PubMed, the Epistemonokos COVID-19 platform (COVID-19 L·OVE), and the WHO Global Research on COVID-19 Database to locate quantitative, qualitative, and mixed methods studies reviews. Reviews that meet the inclusion criteria will undergo quality assessment (AMSTAR) and data extraction. Two reviewers will independently conduct title and abstract screening and extract and synthesize the data. Disagreements will be resolved through full team discussion. Subgroup analyses will be performed to compare findings according to social indicators of target populations, country location of the first author, and other contextual factors. Thematic analysis and synthesis will be used to “transform the data” into themes by applying a deductive-inductive approach. Frequency distributions will be calculated to assess the strength of support for each theme. Findings will be presented in tabular and narrative forms to facilitate their interpretation. Significance: Informed consent is a fundamental bioethical principle in medical research and practice. Insufficient attention to the concerns of vaccine recipients about these matters, compounded by a neglect to discuss the evidence-base informing these concerns, may contribute to the very problem that the COVID-19 “vaccine hesitancy” expert literature purports to address. This is especially true of an intervention based on novel technologies and intended to be delivered on a global scale. Identifying if and how the expert literature engages with these concerns is critical. Systematic review registration: PROSPERO CRD42022351489. [1] Although we use the phrase “COVID-19 vaccines” throughout, we believe they should more appropriately be referred to as “COVID-19 genetic vaccines”, “COVID-19 injections”, or "mRNA biologicals”. However, we have chosen “vaccine” with no quotation marks for better readability. For an in-depth discussion of this issue, see Rose (2021).
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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 | 0.085 | 0.118 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.013 |
| Bibliometrics | 0.021 | 0.014 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.075 | 0.009 |
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".