COVID-19 and ‘immune boosting’ on the internet: a content analysis of Google search results
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: The spread of misinformation has accompanied the coronavirus pandemic, including topics such as immune boosting to prevent COVID-19. This study explores how immune boosting is portrayed on the internet during the COVID-19 pandemic. DESIGN: Content analysis. METHODS: We compiled a dataset of 227 webpages from Google searches in Canada and the USA using the phrase 'boost immunity' AND 'coronavirus' on 1 April 2020. We coded webpages for typology and portrayal of immune boosting and supplements. We recorded mentions of microbiome, whether the webpage was selling or advertising an immune boosting product or service, and suggested strategies for boosting immunity. RESULTS: No significant differences were found between webpages that appeared in the searches in Canada and the USA. The most common types of webpages were from news (40.5%) and commercial (24.7%) websites. The concept of immune boosting was portrayed as beneficial for avoiding COVID-19 in 85.5% of webpages and supplements were portrayed as beneficial in 40% of the webpages, but commercial sites were more likely to have these portrayals. The top immune boosting strategies were vitamin C (34.8%), diet (34.4%), sleep (34.4%), exercise (30.8%) and zinc (26.9%). Less than 10% of the webpages provide any critique of the concept of immune boosting. CONCLUSIONS: Pairing evidence-based advice for maintaining one's health (eg, healthy diet, exercise, sleep) with the phrase immune boosting and strategies lacking in evidence may inadvertently help to legitimise the concept, making it a powerful marketing tool. Results demonstrate how the spread of misinformation is complex and often more subtle than blatant fraudulent claims.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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