‘I Live With Lynch. Cancer Worry Ebbs Into the Background, Then Something Brings It to the Fore.’ A Qualitative Interview Study Exploring How Lynch Syndrome Carriers Make Sense of Their Cancer Risks and Implications to Support Decision Making
Bibliographic record
Abstract
BACKGROUND: Lynch syndrome carriers ('carriers') are presented with complex, emotionally laden choices regarding management of increased genetic cancer risks. Decision aids encourage active involvement in values-based health decisions. This paper aimed to address the research question: How do Lynch syndrome carriers make sense of their chances of developing cancer, and what are the implications for providing support with decision making about genetic cancer risk management? METHODS: Adult carriers were recruited through a genetics service or involvement with Lynch Syndrome UK. Semi-structured interviews explored lived experiences of carriers' access to care with a focus on decision support. Themes were constructed using framework analysis. These were developed into a conceptual model with recommendations for codevelopment of improved information and support including a tailored decision aid to complement integrated healthcare. RESULTS: Twenty participants included 12 women and eight men, half with a history of cancer. Six overarching themes were: (1) finding balance with Lynch; (2) living 'on higher alert'; (3) managing uncertainty: 'I've thought about it a lot'; (4) burden of responsibility: 'It's on me'; (5) access to joined-up care and support: 'There's something missing'; and (6) influence/pressure from others. CONCLUSIONS: This qualitative interview study provided in-depth insights from Lynch syndrome carriers about their lived experiences, informed by their values. Recommendations to empower carriers to make sense of genetic cancer risks and support decisions included accessible, trusted information, educated healthcare professionals, shared decision making, and joined-up integrated care pathways complemented by tailored decision aids.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".