Piloting a Spanish-Language Web-Based Tool for Hereditary Cancer Genetic Testing
Bibliographic record
Abstract
The delivery of hereditary cancer pre-test education among Spanish-language patients is impeded by the dearth of Spanish-speaking genetic counselors. To address this gap, we evaluated a web-based genetic education tool delivered in Spanish to provide information typically discussed during an initial genetic counseling session. Spanish-speaking patients with a personal or family history of cancer were recruited at two centers in Puerto Rico and through social media. A total of 41 participants completed a survey before and after viewing the tool to measure knowledge, attitudes, and decisional empowerment. A subset of 10 participants completed a virtual semi-structured interview to assess the usability and appropriateness of the tool. Paired t-tests were calculated to evaluate changes in knowledge and attitudes. A McNemar test assessed for decisional empowerment. Interview transcripts were translated from Spanish to English and inductively coded and analyzed. Results revealed significant increases in knowledge (p < 0.001), while attitudes about genetic testing did not change (p = 0.77). The proportion of individuals who felt fully informed and empowered to decide about whether to undergo genetic testing increased from 15% to 51% (p < 0.001). Qualitative data indicated that participants found the tool easy to use with informative and valuable content. Our findings suggest this Spanish-language tool is a user-friendly and scalable solution to help inform and empower many individuals to decide about cancer genetic testing, recognizing that others may still benefit from genetic counseling prior to testing.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".