Which factors drive the choice of the French‐speaking Quebec population towards a COVID‐19 vaccination programme: A discrete‐choice experiment
Bibliographic record
Abstract
OBJECTIVES: The aims of this study were to elicit preferences about the coronavirus disease 2019 (COVID-19) vaccine campaign in the general French-speaking adult Quebec population and to highlight the characteristics of the vaccine campaign that were of major importance. METHODS: A discrete-choice experiment (DCE) was conducted between April and June 2021, in Quebec, Canada. A quota sampling method by age, gender and educational level was used to achieve a representative sample of the French-speaking adult population. The choice-based exercise was described by seven attributes within a vaccine campaign scenario. A mixed logit (MXL) model and a latent class logit (LCL) model were used to derive utility values. Age, gender, educational level, income and fear of COVID-19 were included as independent variables in the LCL. RESULTS: A total of 1883 respondents were included for analysis, yielding 22,586 choices. From these choices, 3425 (15.16%) were refusals. In addition, 1159 (61.55%) individuals always accepted any of the vaccination campaigns, while 92 individuals (4.89%) always refused vaccine alternatives. According to the MXL, relative weight importance of attributes was effectiveness (32.50%), risk of side effects (24.76%), level of scientific evidence (22.51%), number of shots (15.73%), priority population (3.60%), type of vaccine (0.61%), and vaccination location (0.28%). Four classes were derived from the LCL model and attributes were more or less important according to them. Class 1 (19.8%) was more concerned about the effectiveness (27.99%), safety (24.22%) and the number of shots (21.82%), class 2 (55.3%) wanted a highly effective vaccine (40.16%) and class 3 (17.6%) gave high value to the scientific evidence (42.00%). Class 4 preferences (7.4%) were more balanced, with each attribute having a relative weight ranging from 1.84% (type of vaccine) to 21.32% (risk of side effects). Membership posterior probabilities to latent classes were found to be predicted by individual factors such as gender, annual income or fear of COVID-19. CONCLUSIONS: Vaccination acceptance relies on multiple factors. This study allowed assessment of vaccination-specific issues through a choice-based exercise and description of factors influencing this choice by segmenting the sample and drawing profiles of individuals. Moreover, besides effectiveness and safety, a major point of this study was to show the importance given by the general population to the level of scientific evidence surrounding vaccines. PATIENT OR PUBLIC CONTRIBUTION: A small group of citizens was involved in the conception, design and interpretation of data. Participants of the DCE were all from the general population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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".