311 Decisional needs of Canadians considering vaccination during the covid-19 pandemic: a population-based cross-sectional survey
Bibliographic record
Abstract
Introduction Since the first COVID-19 vaccine was approved in December 2020, Canadians faced the decision of whether or not to get the COVID-19 vaccination. We sought to identify Canadians’ decisional needs about COVID-19 vaccination. Methods We conducted two online surveys of adult Canadians (aged≥18) to explore decisions and decisional needs during the first two years of the COVID-19 pandemic (1454 participants in May 2021; 1718 in May 2022). This is a sub-analysis of participants who identified the COVID-19 vaccination as a difficult decision. In the sub-analysis, we assessed the decisional needs using questions informed by the Ottawa Decision Support Framework including Decisional Conflict and Decision Regret Scales. We analyzed data descriptively. We reported the methods according to the Checklist for Reporting Results of Internet E-Surveys (CHERRIES) statement. Results In the first and second year, 490 (33.7%) and 465 (27.6%) participants respectively selected the decision about COVID-19 vaccination. The rate of clinically significant decision conflict (CSDC, score >37.5 out of 100) was 22.7% and 26.3%. Common factors influencing the decision were: worried about choosing the ‘wrong’ option (45.5%; 32.0%), difficulty separating fake news/fake science results from scientific evidence (39.4%; 30.9%) and worry about getting COVID-19 (30.4%; 30.9%). Of 440 and 463 participants who had made a decision, 23.9% and 38.4% had moderate to severe decision regret (score >25 out of 100). Discussion Many Canadians who faced the decision about COVID-19 vaccination experience clinically significant decisional conflict. Decision regret increased in the second year. This decision was influenced by uncertainty, misinformation, and fear of getting COVID-19. Conclusion Decision support interventions are needed to address the decisional needs of Canadians considering new COVID-19 vaccination.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".