Evaluation of an Educational Health Website on Infections and Antibiotics in England: Mixed Methods, User-Centered Approach
Bibliographic record
Abstract
BACKGROUND: e-Bug, an educational health website for teachers and students, aims to help control antibiotic resistance by educating young people about microbes, hygiene, and antibiotic resistance, reducing the incidence of infection and, therefore, the need for antibiotics. The teachers' section of the e-Bug website has not been evaluated since it was launched in 2009, and worldwide page views have been steadily decreasing since 2013. OBJECTIVE: This study aimed to apply GoodWeb, a comprehensive framework utilizing methodologies and attributes that are relevant to the digital era, to evaluate and suggest improvements to the e-Bug website. METHODS: Electronic questionnaires and face-to-face completion of task scenarios were used to assess content, ease of use, interactivity, technical adequacy, appearance, effectiveness, efficiency, and learnability of the teachers' section of the e-Bug website. RESULTS: A total of 106 teachers evaluated the e-Bug website; 97.1% (103/106) of them reported that they would use e-Bug, and 98.1% (104/106) of them reported that they would recommend it to others. Participants thought that there was a niche for e-Bug because of the way the resources fit into the national curriculum. Suggestions for improvements included changing the menu indication by highlighting the current page or deactivating links, improving home page indication, and providing a preview of resources when hovering the mouse over hyperlinks. Additional features requested by users included a search function and access to training opportunities. CONCLUSIONS: This paper reports that the GoodWeb framework was successfully applied to evaluate the e-Bug website, and therefore, it could be used to guide future website evaluations in other fields. Results from this study will be used to appraise the current quality and inform any future changes, modifications, and additions to e-Bug.
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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 | 0.051 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".