Assessing and Responding in Real Time to Online Anti-vaccine Sentiment during a Flu Pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The perceived safety of vaccination is an important explanatory factor for vaccine uptake and, consequently, for rates of illness and death. The objectives of this study were (1) to evaluate Canadian attitudes around the safety of the H1N1 vaccine during the fall 2009 influenza pandemic and (2) to consider how public health communications can leverage the Internet to counteract, in real time, anti-vaccine sentiment. We surveyed a random sample of 175,257 Canadian web users from October 27 to November 19, 2009, about their perceptions of the safety of the HINI vaccine. In an independent analysis, we also assessed the popularity of online flu vaccine-related information using a tool developed for this purpose. A total of 27,382 unique online participants answered the survey (15.6% response rate). Of the respondents, 23.4% considered the vaccine safe, 41.4% thought it was unsafe and 35.2% reported ambivalence over its safety. Websites and blog posts with anti-vaccine sentiment remained popular during the course of the pandemic. Current public health communication and education strategies about the flu vaccine can be complemented by web analytics that identify, track and neutralize anti-vaccine sentiment on the Internet, thus increasing perceived vaccine safety. Counter-marketing strategies can be transparent and collaborative, engaging online "influencers" who spread misinformation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 it