Information needs of women with BRCA mutations regarding cancer risk management and decision-making
Bibliographic record
Abstract
PURPOSE: Providing women who have tested positive for a pathogenic variant in BRCA1 or BRCA 2 relevant information can help them to make informed decisions about managing their cancer risk. However, there is a lack of targeted informational support for BRCA positive women specific to the Irish context. The objective of this study is to identify the information needs of women diagnosed with a pathogenic variant in BRCA1 or BRCA 2 regarding cancer risk management and decision-making. METHODS: This is a descriptive qualitative study. Participants were recruited using purposive sampling and included women with a pathogenic variant in BRCA1 or BRCA2 without a history of breast or ovarian cancer. Two focus groups were held with women (n = 16) to enable them to generate ideas and understanding of their shared information needs. In addition, ten individual interviews were conducted to capture the additional perspectives of health care and relevant policy stakeholders. Interviews were analysed using inductive coding (Braun and Clarke, 2006), with NVivo software (Qsr international, 1999). RESULTS: Three main themes were identified, Cancer Risk Management, Receiving Information, and Implications to Health and Wellbeing. BRCA-positive women expressed a need for information about managing their cancer risk. They were particularly concerned with managing the impact of cancer risk-reducing interventions on their psychological and physical health, wellbeing, and family life. Many women felt they had to advocate for themselves to get treatment and receive information. Participants expressed a need for a comprehensive informational resource where all relevant information related to BRCA risk management could be accessed at a single location. CONCLUSION: This study suggests that women diagnosed with a pathogenic variant in BRCA1 or BRCA2 in Ireland need more accessible information about managing their cancer risk, and the impact of a BRCA diagnosis on their family, health and wellbeing. These results will be used to identify relevant content for developing an informational decision aid for Irish women.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".