Breast Cancer Surgery Experiences and Outcomes in Sex and Gender Minority Individuals: A Systematic Review
Bibliographic record
Abstract
Abstract Purpose of the study: The purpose of this study was to summarize and critique contemporary research describing the experiences and outcomes of sex and gender minority (SGM) individuals with breast cancer surgery through systematic literature review. Methods: We performed a comprehensive search using keywords and subject headings to elicit studies that addressed experiences and outcomes of SGM breast cancer survivors in PubMed, Embase, CINAHL, PsycINFO, and LGBT Life. Studies were included if (1) participants were breast cancer survivors who identified as sexual and gender minority (SGM) women or transgender men; (2) one or more of the following were reported: experiences, preferences, or needs related to breast cancer care or surgical treatment decision-making, as well as interactions with healthcare providers and support persons; (3) they were published in English; and (4) they were published in the last 20 years. Covidence® was used to document the inclusion/exclusion process. Included studies were assessed using The Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Qualitative Research. The authors performed thematic content analysis to identify emergent themes. Results: The search yielded 115 records, and seven studies were included in the final critical appraisal. All studies were qualitative with sample sizes ranging n=10–81, and quality scores ranged 6–8 out of 10. Studies were performed in the U.S. and Canada, and included sexual and gender diverse individuals. Major themes were identified: 1) surgical decision-making, 2) experiences of the post-surgical body, 3) information and support seeking, and 4) interactions with healthcare providers. Subthemes are explored, including body image vs. function, gender policing and politicizing of the body, and intersectionality. Conclusions: SGM breast cancer survivors have unique experiences of healthcare access, decision-making, and quality of life in survivorship. Researchers and clinicians must consider SGM breast cancer survivors' personal values and preferences for treatment, as well as their support network. Culturally sensitive healthcare provider interactions are critical for reducing health disparities in cancer care access and quality of life outcomes.
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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 | 0.015 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".