An Accessible Communication System for Population-Based Genetic Testing: Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: Genetic testing uptake is low, despite the well-established connection between pathogenic variants in certain cancer-linked susceptibility genes and ovarian cancer risk. Given that most major insurers cover genetic testing for those with a family history suggestive of hereditary cancer, the issue may lie in access to genetic testing. Remotely accessible web-based communication systems may improve awareness, and uptake, of genetic testing services. OBJECTIVE: This study aims to present the development and formative evaluation of the multistep web-based communication system required to support the implementation of, and access to, genetic testing. METHODS: While designing the multistep web-based communication system, we considered various barriers and facilitators to genetic testing, guided by dimensions of accessibility. In addition to conducting usability testing, we performed ongoing assessments focusing on the function of the web-based system and participant response rates, with the goal of continuing to make modifications to the web-based communication system as it is in use. RESULTS: The combined approach of usability testing and expert user experience consultation resulted in several modifications to the multistep web-based communication system, including changes that related to imagery and content, web accessibility, and general organization of the web-based system. All recommendations were made with the goal of improving the overall accessibility of the web-based communication system. CONCLUSIONS: A multistep web-based communication system appears to be an effective way to address many potential barriers to access, which may otherwise make genetic testing difficult for at-risk individuals to participate in. Importantly, some dimensions of access were easy to assess before study recruitment, but other aspects of the communication system required ongoing assessment during the implementation process of the Making Genetic Testing Accessible study.
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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 | 0.032 | 0.057 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".