Surfing the web during pandemic flu: availability of World Health Organization recommendations on prevention
Bibliographic record
Abstract
BACKGROUND: People often search for information on influenza A(H1N1)v prevention on the web. The extent to which information found on the Internet is consistent with recommendations issued by the World Health Organization is unknown. METHODS: We conducted a search for "swine flu" accessing 3 of the most popular search engines through different proxy servers located in 4 English-speaking countries (Australia, Canada, UK, USA). We explored each site resulting from the searches, up to 4 clicks starting from the search engine page, analyzing availability of World Health Organization recommendations for swine flu prevention. RESULTS: Information on hand cleaning was reported on 79% of the 147 websites analyzed; staying home when sick was reported on 77.5% of the websites; disposing tissues after sneezing on 75.5% of the websites. Availability of other recommendations was lower. The probability of finding preventative recommendations consistent with World Health Organization varied by country, type of website, and search engine. CONCLUSIONS: Despite media coverage on H1N1 influenza, relevant information for prevention is not easily found on the web. Strategies to improve information delivery to the general public through this channel should be improved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".