Co-design and user testing of a Japanese encephalitis vaccine decision aid (JEVaDA)
Bibliographic record
Abstract
BACKGROUND: Japanese encephalitis (JE) is a rare but potentially serious infection in travellers. While effective vaccines are available, uptake remains low. Vaccine decision aids are evidence-based tools designed to help users make informed vaccination decisions. This study details the development of a novel web-based Japanese encephalitis vaccine decision aid (JEVaDA) for travellers, following globally recognised standards. METHODS: Collaborating with community members, healthcare providers and experts, we followed a multi-step approach, involving a scoping review, a survey of user needs, co-design workshops, user testing, and expert review. Findings from workshops and testing informed the development of decision aid prototypes, with input from a graphic designer. We used the Patient Education Materials Assessment Tool to assess understandability and actionability and the Ottawa acceptability tool to measure components of acceptability. The final version was adapted to a web-based format. RESULTS: Five co-design workshops conducted with 16 participants (nine community members, seven healthcare providers) gathered input and feedback on the initial PDF prototype. The refined prototype was user-tested by another group of 22 participants (16 community members, six healthcare providers) and reviewed by five subject matter experts. Feedback indicated areas for improvement in risk visualisation, personalised content, and catering to diverse user needs. The decision aid scored highly for understandability (89 %) and actionability (87 %). All participants (100 %) found it suitable for decision making. CONCLUSION: We successfully co-designed and user-tested a JE vaccine decision aid with community members, healthcare providers and experts. The interactive, web-based version is now freely available at www.monash.edu/vaccinedecisionaids-je.
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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 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".