Breast Reconstruction following Mastectomy for Breast Cancer: The Decisions of Sexual Minority Women
Bibliographic record
Abstract
BACKGROUND: Prior research on decision-making for reconstructive surgery after mastectomy has not addressed the specific considerations of sexual minority women (women who partner with women, and lesbian or bisexual identified women). The purpose of this study is to explore which issues sexual minority women considered when making decisions on reconstructive surgery and to understand the influence and perspectives of these women's most important support persons. METHODS: Study participants were recruited through targeted community-based sampling. The authors conducted individual semistructured interviews with 15 sexual minority women who had been treated with mastectomy after breast cancer diagnosis and 12 support persons who were identified by these women as their most important source of support. Using qualitative data analysis software, transcribed interviews were analyzed. Through constant comparison methods, themes related to the decision on and experiences and satisfaction with reconstructive choice were identified from the narrative data. RESULTS: The considerations of women who decided for or against reconstruction are rooted in a value system and body image shaped by their sexual minority identity. Women who chose reconstruction experienced difficulties and regrets, whereas women without reconstruction adjusted well after time. Partners of sexual minority women matched the level of satisfaction with reconstructive choice achieved by the women themselves. CONCLUSION: Providers who treat sexual minority women might benefit from knowing about issues important to this population to provide more comprehensive care.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".