1371. Results From the COVID-19 Vaccines Discrete Choice Experiment Pre-Test Qualitative Interviews in Canada, Germany, the UK, and US General Population
Notice bibliographique
Résumé
Abstract Background COVID-19 vaccine preferences can influence vaccine coverage. Discrete choice experiments (DCE) can be used to elicit people’s trade-offs. DCEs require evidence-based attribute selection and validation of understanding with lay audiences. To inform a future DCE, a pre-test was conducted to examine the survey and refine six attributes selected from a targeted literature review and expert interviews. Methods Interviews were conducted in March 2023 in Canada, Germany, the UK, and US. Self-reported anti-vaccinationists were excluded. Eligible individuals were interviewed during the completion of a survey that included 11 choice tasks and supplementary questions. The “think aloud” method was used to evaluate participants’ understanding of the survey and if they were making trade-offs as hypothesized. Four country-level experts validated the survey modifications based on the results. Results Six phone interviews were completed in each country (N=24). Mean age was 43.7; 50% were women; 50% reported receiving the full COVID-19 vaccine series; 45.8% received the initial series but were unsure about additional doses; 1 was unvaccinated (4.2%). Participants’ top four priorities were vaccine protection against COVID-19, serious side-effects, protection against severe COVID-19, and common side-effects, followed by vaccine type and timing of COVID-19/influenza vaccines (Fig 1). More than half of the participants would consider co-administration of COVID-19 and influenza vaccines, either as two separate injections (58.3%) or as a single, combined injection (62.5%) (Fig 2a). Most individuals (54.2%) preferred an annual COVID-19 vaccine; over every 6 months (4.2%), and 20.8% were indifferent (Fig 2b). When deciding to get vaccinated, most considered the following to be important: how long a vaccine was examined in humans (65.2%), how long a vaccine was used in a vaccination program (62.5%); 50% considered vaccine type (mRNA or protein subunit) important (Fig 3). Conclusion This study validated the importance of key vaccine attributes driving people’s choices and feedback was used to improve the clarity of attribute descriptions. A future DCE will be fielded to increase the understanding of COVID-19 vaccine preference and hesitancy. Disclosures Sumitra Sri Bhashyam, MSc, PhD, Novavax Inc: Grant/Research Support L.G Shane, Pharm.D., RPH., BScPharm., Novavax Inc: Employee of Novavax Inc|Novavax Inc: Stocks/Bonds Hannah B. Lewis, MSc, PhD, Novavax Inc: Grant/Research Support Marie de la Cruz, MS, Novavax Inc: Grant/Research Support Jayne Galinsky, PhD, Novavax Inc: Grant/Research Support Keeva Demchuk, n/a, Novavax Inc: Grant/Research Support Nancy M. Waite, Waite PharmD FCCP, GSK: Advisor/Consultant|Novavax Inc: Honoraria|Pfizer: Advisor/Consultant|Sanofi: Advisor/Consultant|Sanofi: Grant/Research Support Jeffrey V. Lazarus, PhD, MIH, MA, AbbVie: Advisor/Consultant|AbbVie: Conference travel|Gilead Sciences: Advisor/Consultant|Gilead Sciences: Grant/Research Support|Gilead Sciences: Honoraria|Moderna: Honoraria|Novavax Inc: Advisor/Consultant|Novavax Inc: Honoraria|Novo Nordisk: Honoraria|Roche Diagnostics: Grant/Research Support David M. Salisbury, CB FMedSci FRCP FRCPCH FFPH, Clover Pharmaceuticals: Advisor/Consultant|GSK: Advisor/Consultant|Moderna: Advisor/Consultant|Novavax Inc: Honoraria|Sanofi: Advisor/Consultant
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».