Perspectives of Women Considering Bilateral Prophylactic Mastectomy and their Peers towards a Telephone‐Based Peer Support Intervention
Bibliographic record
Abstract
Prophylactic mastectomy is an effective strategy to reduce the risk of breast cancer for women carrying a BRCA1/2 germline mutation. This decision is complex and may raise various concerns. Women considering this surgery have reported their desire to discuss the implications of this procedure with women who have undergone prophylactic mastectomy. We conducted a qualitative study to describe the topics covered during a telephone-based peer support intervention between women considering prophylactic mastectomy (recipients) and women who had undergone this surgery (peers), and to explore their perspectives regarding the intervention. Thirteen dyads were formed and data from participant logbooks and evaluation questionnaires were analyzed using a thematic content analysis. Three main dimensions emerged: physical, psychological, and social. The most frequent topics discussed were: surgery (92%), recovery (77%), pain and physical comfort (69%), impacts on intimacy and sexuality (54%), cancer-related anxiety (54%), experience related to loss of breasts (46%). Peers and recipients report that sharing experiences and thoughts about prophylactic mastectomy and the sense of mutual support within the dyad contributed significantly to their satisfaction. Special attention should be paid to the similarities between personal and medical profiles in order to create harmonious matches.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".