Optimizing risk‐reducing surgery and aspirin decision aids for Lynch syndrome carriers using the person‐based approach: A think‐aloud interview study
Bibliographic record
Abstract
Lynch syndrome "carriers" carry a germline pathogenic variant conferring gene-, sex-, and organ-specific increased cancer risks. They are presented with difficult, interrelated choices over their lifetime. This study was part of a larger project to codesign a health intervention, Lynch Choices™ https://canchoose.org.uk to provide an information hub and decision support for carriers, their family members, and clinicians. This study aimed to answer the research question: What content, framing, and design elements of a decision aid for genetic cancer risk management are important to Lynch syndrome carriers? Adult carriers were invited to a think-aloud interview to hear their thoughts about a prototype version of Lynch Choices™ containing values-clarification exercises. The first half of interviews focused on the gynecological risk-reducing surgery and the second half on the aspirin decision aid. Twenty carriers (eight men) were interviewed, half of whom had a personal history of cancer. Iterative refinement of Lynch Choices™ content and design was completed between interviews using a transparent table of changes from the person-based approach. Following the interviews, reflexive thematic analysis was applied to the entire qualitative dataset. Three themes were constructed to guide further optimization and make recommendations for improved cancer risk communication in clinical practice. The three themes were: (1) Interpreting gene-specific cancer risks and "What does it mean to me?"; (2) Words matter: Careful phrasing is important to feel understood; (3) Decision aids: They can help but might trigger emotions. Think-aloud interviews provided in-depth insight into the psychosocial context of carriers. This informed optimization of the decision aid to support engagement and promote shared decision making with healthcare professionals. The learning from this study had broader implications beyond decision aid development, to understanding preferences, needs, and experiences regarding genetic cancer risk communication and decision support.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".