Optimization of a Web-Based Self-Assessment Tool for Preconception Health in People of Reproductive Age in Australia: User Feedback and User-Experience Testing Study
Bibliographic record
Abstract
Background: Good preconception health reduces the incidence of preventable morbidity and mortality for women, their babies, and future generations. In Australia, there is a need to increase health literacy and awareness about the importance of good preconception health. Digital health tools are a possible enabler to increase this awareness at a population level. The Healthy Conception Tool (HCT) is an existing web-based, preconception health self-assessment tool, that has been developed by academics and clinicians. Objective: This study aims to optimize the HCT and to seek user feedback to increase the engagement and impact of the tool. Methods: In-depth interviews were held with women and men aged 18-41 years, who spoke and read English and were residing in Australia. Interview transcripts were analyzed, and findings were used to inform an enhanced HCT prototype. This prototype underwent user-experience testing and feedback from users to inform a final round of design changes to the tool. Results: A total of 20 women and 5 men were interviewed; all wanted a tool that was quick and easy to use with personalized results. Almost all participants were unfamiliar with the term "preconception care" and stated they would not have found this tool on the internet with its current title. User-experience testing with 6 women and 5 men identified 11 usability issues. These informed further changes to the tool's title, the information on how to use the tool, and the presentation of results. Conclusions: Web-based self-assessment tools need to be easy to find and should communicate health messages effectively. End users' feedback informed changes to improve the tool's acceptability, engagement, and impact. We expect that the revised tool will have greater reach and prompt more people to prepare well for pregnancy.
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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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".