Novel Psychosocial Correlates of COVID-19 Vaccine Hesitancy: Cross-Sectional Survey
Notice bibliographique
Résumé
BACKGROUND: Effective COVID-19 vaccines have been available since early 2021 yet many Americans refuse or delayed uptake. As of mid-2022, still around 30% of US adults remain unvaccinated against COVID-19. The majority (81%) of these unvaccinated adults say they will "definitely not" be getting the COVID-19 vaccine. Understanding the determinants of COVID-19 vaccine uptake is critical to reducing death and illness from the virus, as well as to inform future vaccine efforts, such as the more recent bivalent (omicron) booster. OBJECTIVE: This study aimed to expand our understanding of psychosocial determinants of COVID-19 vaccine uptake. We focus on both COVID-19-specific factors, such as COVID-19 conspiracy beliefs, as well as more global personality attributes such as dogmatism, reactance, gender roles, political beliefs, and religiosity. METHODS: We conducted a web-based survey in mid-2021 of a representative sample of 1376 adults measuring both COVID-19-specific beliefs and attitudes, as well as global personality attributes. COVID-19 vaccination status is reported at 3 levels: vaccinated; unvaccinated-may-get-it; unvaccinated-hard-no. RESULTS: Our analyses focused on the correlation of COVID-19 vaccination status with 10 psychosocial attributes: COVID-19-specific conspiracy theory beliefs; COVID-19 vaccine misinformation; COVID-19-related Rapture beliefs; general antivaccination beliefs; trait reactance; trait dogmatism; belief in 2020 election fraud; belief in a QAnon conspiracy; health care system distrust; and identification with traditional gender roles. We used a multivariate analysis of covariance to examine mean differences across vaccine status groups for each of the correlates while holding constant the effects of age, gender, race, income, education, political party, and Evangelicalism. Across the 10 psychosocial correlates, several different response scales were used. To allow for comparison of effects across correlates, measures of effect size were computed by converting correlates to z scores and then examining adjusted mean differences in z scores between the groups. We found that all 10 psychosocial variables were significantly associated with vaccination status. After general antivaccination beliefs, COVID-19 misinformation beliefs and COVID-19 conspiracy beliefs had the largest effect on vaccine uptake. CONCLUSIONS: The association of these psychosocial factors with COVID-19 vaccine hesitancy may help explain why vaccine uptake has not shifted much among the unvaccinated-hard-no group since vaccines became available. These findings deepen our understanding of those who remain resistant to getting vaccinated and can guide more effective tailored communications to reach them. Health communication professionals may apply lessons learned from countering related beliefs and personality attributes around issues such as climate change and other forms of vaccine hesitancy. For example, using motivational interviewing strategies that are equipped to handle resistance and provide correct information in a delicate manner that avoids reactance.
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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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».