Understanding the influence of online information, misinformation, disinformation and reinformation on COVID-19 vaccine acceptance: Protocol for a multicomponent study (Preprint)
Bibliographic record
Abstract
BACKGROUND The COVID-19 pandemic generated an explosion in the amount of information shared online, including false and misleading information on the virus, and recommended protective behaviours. Prior to the pandemic, online mis- and disinformation were already identified as having an impact on people’s decision to refuse or delay recommended vaccination for themselves or their children. OBJECTIVE The overall aim of this study is to better understand the influence of online mis- and disinformation on COVID-19 decisions and investigate potential solutions to reduce the impact of online mis- and disinformation about vaccines. METHODS Based on different research approaches, this study involves 1) the use of artificial intelligence techniques, 2) a online survey, 3) interviews and, 4) a scoping review and an environmental scan of the literature. RESULTS As of September 1st, 2022, data collection is completed for all objectives. Analysis is being conducted and results should be disseminated in the upcoming months. CONCLUSIONS Findings from this study will help understand the underlying determinants of vaccine hesitancy among Canadian individuals and identify effective tailored interventions to improve vaccine acceptance among them. CLINICALTRIAL
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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 | 0.035 | 0.070 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.100 | 0.016 |
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".