Genetics Adviser: The development and usability testing of a new patient digital health application to support clinical genomic testing
Bibliographic record
Abstract
Purpose: Increasing demand for genomic testing coupled with genetics workforce shortages has placed unsustainable pressure on standard models of care. Digital tools can offer improved access, efficiency, and cost savings. We created a patient-facing digital health application to support genomic testing. Methods: We developed the digital application through user-centered design, guided by an advisory board. We tested its usability and acceptability with patients, practitioners, and members of the general public using mixed methods; data were analyzed using qualitative description and descriptive statistics. Results: The "Genetics Adviser" delivers pre-test education, counseling, and post-test return of results adaptable to any population, test platform, and setting. Usability testing with 25 patients, the general public, and genetics practitioners (15/25 female; mean age range 40-49 years) demonstrated enthusiasm about the application; users found it easy to navigate and comprehend. Acceptance testing with 19 patients and the public (13/19 female; mean age range 40-49 years) indicated high acceptability of the application and moderate knowledge of genomic sequencing after use. Conclusion: The Genetics Adviser is a comprehensive, interactive, patient-centered application found to have high acceptability and usability for pre- and post-test genomic testing, counseling, and return of results adaptable for multiple testing platforms, populations, and settings.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.007 | 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".