Monitoring Web Site Usage of e-Bug: A Hygiene and Antibiotic Awareness Resource for Children
Bibliographic record
Abstract
BACKGROUND: e-Bug is an educational resource which teaches children and young people about microbes, hygiene, infection, and prudent antibiotic use. The e-Bug resources are available in over 22 different languages and they are used widely across the globe. The resources can be accessed from the e-Bug website. OBJECTIVE: The objective of this study was to analyze the usage of the e-Bug website in order to understand how users access the website, where and when they access the site, and to review variation in use across the different areas of the site. METHODS: The usage statistics for the e-Bug website were monitored by Google Analytics between September 2010 and August 2013. RESULTS: The statistics show the website had over 324,000 visits during the three years, from just under 250,000 visitors, with the number of visitors increasing year after year. Visitors accessed the website from 211 different countries, with more than 267,000 documents downloaded. The majority of visitors were from the United Kingdom and visited the English website, although countries such as France and Portugal were also frequent visitors. CONCLUSIONS: These website statistics confirm that e-Bug is frequently used across Europe and highlight that e-Bug use has expanded across the world. The findings from this report will be used to inform future modifications or updates to the materials, as well as the development of new educational resources.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".