Identifying Population Segments by Differing Levels of COVID-19 Vaccine Confidence and Evaluating Subsequent Uptake of COVID-19 Prevention Behaviors: Web-Based, Longitudinal, Probability-Based Panel Survey
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic prompted the launch of the US Department of Health and Human Services' COVID-19 Public Education Campaign to boost vaccine confidence and uptake among adults, as vaccines are key to preventing severe illness and death. OBJECTIVE: Past segmentation research relevant to COVID-19 behavior has found important differences in attitudes, sociodemographics, and subsequent COVID-19 prevention behaviors across population segments. This study extends prior work by incorporating a more comprehensive set of attitudes, behaviors, and sociodemographic variables to identify population segments by differing levels of COVID-19 vaccine confidence and evaluate differences in their subsequent uptake of COVID-19 prevention behaviors. METHODS: Data were obtained from 5 waves (January 2021 to June 2022) of a web-based longitudinal, probability-based panel survey of US adults (N=4398) administered in English and in Spanish. Participants were recruited from NORC at the University of Chicago's national AmeriSpeak panel and were invited to participate across multiple waves. Latent class cluster analysis estimated segments of respondents based on over 40 COVID-19 attitudes, beliefs, behaviors, and sociodemographics as reported in wave 1. Survey-weighted cross-tabulations and bivariate regression analyses assessed differences in COVID-19 vaccine uptake, booster uptake, mask use, and social distancing in all segments across all 5 survey waves. RESULTS: A total of 6 segments (hardline nonintenders, prevention-compliant nonintenders, burned-out waiters, anxious waiters, skeptical confidents, and ready confidents) were identified, which differed by their COVID-19 vaccine confidence, prevention-related attitudes and behaviors, and sociodemographics. Cross-tabulations and regression results indicated significant segment membership differences in COVID-19 vaccine and booster timing, mask use, and social distancing. Results from survey-weighted cross-tabulations comparing COVID-19 vaccine and booster uptake across segments indicate statistically significant differences in these outcomes across the 6 segments (P<.001). Results were statistically significant for each segment (P<.01 for booster uptake among burned-out waiters; P<.001 for all other coefficients), indicating that, on average, respondents in segments with lower intentions to vaccinate reported later receipt of COVID-19 vaccines and boosters relative to the timing of vaccine and booster uptake among ready confidents. CONCLUSIONS: Results extend previous research by showing that initial beliefs and behaviors relevant to COVID-19 vaccination, mask use, and social distancing are important for understanding differences in subsequent compliance with recommended COVID-19 prevention measures. Specifically, we found that across respondent segments, the probability of vaccine and booster uptake corresponded with both COVID-19 vaccine confidence and mask use and social distancing compliance; more compliant segments were more likely to get vaccinated or boosted than less compliant segments given similar levels of vaccine confidence. These findings help identify appropriate audiences for campaigns. Results highlight the use of a comprehensive list of attitudes, behaviors, and other individual-level characteristics that can serve as a basis for future segmentation efforts relevant to COVID-19 and other infectious diseases.
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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| 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 | 0,001 |
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 ».