Developing genetic counseling for male BRCA1/2 mutation carriers based on their own experiences
Bibliographic record
Abstract
Background: Previous studies of genetic counseling (GC) for male BRCA1/2 mutation carriers have focused on their level of satisfaction with the GC and its content. The aim of this study was to examine the GC experiences of male BRCA1/2 mutation carriers, and their suggestions for improving GC, more broadly.Methods: Data were collected by themed interviews of Finnish male BRCA1/2 mutation carriers (n = 31), and subjected to inductive content analysis.Results: The results indicated that the participants had a mixture of both positive and negative experiences of GC regarding operational conditions at Departments of Clinical Genetics (DCGs) and the ability of the counselors’ (clinical geneticists or genetic nurses) to provide GC. Although the GC was implemented in a professional manner, according to the male participants, more concreate and illustrative information should be provided, and the counselors should receive additional training to provide such information and improve their communication skills.Conclusions: Based on results of the study we make some suggestions for tailored GC for male BRCA1/2 mutation carriers. The results may facilitate development of a tentative model of GC that could be extended to broader categories of people at risk of hereditary cancer syndromes in the future.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".