The Risk-Benefit Balance in the COVID-19 “Vaccine Hesitancy” Literature: An Umbrella Review Protocol
Notice bibliographique
Résumé
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).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,085 | 0,118 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,004 |
| Méta-épidémiologie (sens large) | 0,015 | 0,013 |
| Bibliométrie | 0,021 | 0,014 |
| Études des sciences et des technologies | 0,004 | 0,005 |
| Communication savante | 0,009 | 0,009 |
| Science ouverte | 0,005 | 0,007 |
| Intégrité de la recherche | 0,009 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,075 | 0,009 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».