"I'm going to die of something anyway": women's perceptions of tamoxifen for breast cancer risk reduction.
Bibliographic record
Abstract
OBJECTIVE: To investigate how ethnically diverse women who are eligible for tamoxifen prophylaxis because of their breast cancer risk decide about tamoxifen use for risk reduction. DESIGN: A qualitative intervention pilot study used focus groups to discuss the use of tamoxifen and to identify the concerns of ethnically diverse women about the preventive use of this drug. Focus group discussion involved exploration of the benefits and risks of tamoxifen prophylaxis, presentation of a standardized educational intervention, and focused discussion on attitudes about tamoxifen for prevention. Prominent themes emerged from iterative review of focus group transcripts. RESULTS: Fear of breast cancer was not prominent, and participants were less inclined to take tamoxifen as preventive therapy after receiving information. Decisions were based on participants' understandings of competing risks and benefits. Specifically, participants expressed limited willingness to take medication with potential serious side effects for risk reduction and were unwilling to discontinue hormone replacement therapy. Uneasiness about the reliability of scientific studies surfaced in the focus groups comprised of White and Latina women. African-American women described faith as important to prevention. CONCLUSIONS: Women were wary of taking a drug for a disease they might not develop. Women felt they had options other than tamoxifen to reduce their risk of breast cancer, including early detection, diet, faith, and complementary and alternative therapies.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".