Assessment of Health Information About COVID-19 Prevention on the Internet: Infodemiological Study
Bibliographic record
Abstract
BACKGROUND: The internet is a large source of health information and has the capacity to influence its users. However, the information found on the internet often lacks scientific rigor, as anyone may upload content. This factor is a cause of great concern to scientific societies, governments, and users. OBJECTIVE: The objective of our study was to investigate the information about the prevention of coronavirus disease 2019 (COVID-19) on the internet. METHODS: On February 29, 2020, we performed a Google search with the terms "Prevention coronavirus," "Prevention COVID-19," "Prevención coronavirus," and "Prevención COVID-19". A univariate analysis was performed to study the association between the type of authorship, country of publication, and recommendations to avoid COVID-19 according to the World Health Organization (WHO). RESULTS: In total, 80 weblinks were reviewed. Most of them were produced in the United States and Spain (n=58, 73%) by digital media sources and official public health organizations (n=60, 75%). The most mentioned WHO preventive measure was "wash your hands frequently" (n=65, 81%). A less frequent recommendation was to "stay home if you feel unwell" (n=26, 33%). The analysis by type of author (official public health organizations versus digital media) revealed significant differences regarding the recommendation to wear a mask when you are healthy only if caring for a person with suspected COVID-19 (odds ratio [OR] 4.39). According to the country of publication (Spain versus the United States), significant differences were detected regarding some recommendations such as "wash your hands frequently" (OR 9.82), "cover your mouth and nose with your bent elbow or tissue when you cough or sneeze" (OR 4.59), or "stay home if you feel unwell" (OR 0.31). CONCLUSIONS: It is necessary to urge and promote the use of the websites of official public health organizations when seeking information on COVID-19 preventive measures on the internet. In this way, users will be able to obtain high-quality information more frequently, and such websites may improve their accessibility and positioning, given that search engines justify the positioning of links obtained in a search based on the frequency of access to them.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".