“Guys Don’t Have Breasts”: The Lived Experience of Men Who Have BRCA Gene Mutations and Are at Risk for Male Breast Cancer
Bibliographic record
Abstract
Men with BRCA1 or BRCA2 gene mutations are at increased risk of developing breast cancer and may have an indication for breast cancer screening using mammography. Since breast cancer is often viewed as a woman's disease, visibilizing and understanding men's experience of having a BRCA mutation and specifically, of screening for breast cancer through mammography, were the objectives of this research study. The theoretical framework of interpretive phenomenology guided the process of data collection, coding, and analysis. Phenomenology is both a philosophy and research method which focuses on understanding the nature of experience from the perspectives of people experiencing a phenomenon, the essence of and commonalities among people's experiences, and the ways in which people experience the world through their bodies. Data were collected via in-depth interviews with a purposive sample of 15 male participants recruited from the Male Oncology Research and Education (MORE) Program. This article reports findings about participants' use of gender-specific language to describe their breasts, awareness of the ways in which their bodies changed overtime, and experiences of undergoing mammograms. This study is the first to describe men with BRCA's perceptions of their breasts and experiences of mammography in a high-risk cancer screening clinic. This study sheds light on an under-researched area-breasts and masculinities-and could potentially lead to improved clinical understanding of men's embodied experiences of BRCA, as well as suggestions for improving the delivery of male breast cancer screening services.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".