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Record W2173552770 · doi:10.2196/resprot.4049

Monitoring Web Site Usage of e-Bug: A Hygiene and Antibiotic Awareness Resource for Children

2015· article· en· W2173552770 on OpenAlexvenueno aff
Vicki L. Young, Vijayamaharaj Rajapandian, Charlotte Victoria Eley, Beverley Ann Hoekstra, Donna M Lecky, Cliodna McNulty

Bibliographic record

VenueJMIR Research Protocols · 2015
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsnot available
Fundersnot available
KeywordsGlobeResource (disambiguation)World Wide WebAnalyticsUsage dataInternet privacyBusinessGeographyComputer scienceMedicineData science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.334
GPT teacher head0.557
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2015
Admission routes1
Has abstractyes

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