Public Preferences for a COVID-19 Vaccination Program in Quebec: A Discrete Choice Experiment
Bibliographic record
Abstract
OBJECTIVES: We aimed to elicit preferences of the French-speaking Quebec population regarding a COVID-19 vaccination program and to characterize individuals with respect to their vaccination behaviors. METHODS: A discrete choice experiment was conducted in Autumn 2020 via a web-based survey. Its design included seven attributes: vaccine origin, vaccine effectiveness, side effects, protection duration, priority population, waiting time to get vaccinated, and recommender of the vaccine. Utilities were estimated using a mixed-logit model and a latent class logit model. RESULTS: Our sample included 1599 individuals. From this total, 119 always chose the opt-out option (7.4%). According to the mixed-logit model, the relative weights of attributes were as follows: effectiveness (28.48%), side effects (23.68%), protection duration (17.41%), vaccine origin (12.75%), recommender (11.96%), waiting time to get vaccinated (3.62%), and priority population (2.11%). Five classes were derived from the latent class logit model. Class 1 (9.13%) wanted to get vaccinated as fast as possible and was composed of uncertain and more vulnerable individuals. Class 5 (25.14%) was similar to the full sample, mostly favoring vaccination. Classes 2 (7.69%) and 4 (15.82%) included "vaccine hesitant and demanding" individuals but were different in their sociodemographic profiles. Finally, "anti-vaccine" and other "vaccine hesitant" individuals were in class 3 (42.21%). CONCLUSIONS: This study showed the vaccine characteristics that are likely to improve vaccine uptake, which may more easily lead to herd immunity. Different profiles of respondents also showed various levels of acceptance toward a COVID-19 vaccination program, which may help to better understand vaccine hesitancy behaviors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".