Conditions and Consequences of a <i>BRCA</i> Mutation in Young, Single Women of Childbearing Age
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To explore the experiences of young, single women who are at increased risk for hereditary breast and ovarian cancer (HBOC) because of a BRCA mutation. RESEARCH APPROACH: Qualitative. SETTING: Seven states and Canada. PARTICIPANTS: 11 single women aged 18-35 years who tested positive for a BRCA mutation. METHODOLOGIC APPROACH: Grounded theory with in-depth individual interviews conducted via e-mail or telephone. FINDINGS: Analysis resulted in three conditions and three consequences. Conditions were dating or not dating, time in a relationship, and physical impact of surgery or breast cancer treatment. Consequences were explaining their choices, experiencing a sense of urgency, and experiencing a sense of loss. CONCLUSIONS: Young women who are at risk for HBOC face a complex array of decisions after finding out that they carry a BRCA mutation. Being single and childless adds to this complexity. INTERPRETATION: Nurses can listen to young women with HBOC risk, help them clarify their fears and understanding of their risk, and provide nonthreatening support that goes beyond simply providing more information and includes a nonjudgmental understanding of the young women's experience.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 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".