1371. Results From the COVID-19 Vaccines Discrete Choice Experiment Pre-Test Qualitative Interviews in Canada, Germany, the UK, and US General Population
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".