Addressing the Support Needs of Women at High Risk for Breast Cancer: Evidence-Based Care by Advanced Practice Nurses
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To identify support needs of women at high risk for breast cancer and enhance an evidence-based service. DESIGN: Descriptive study. SETTING: A comprehensive, breast-health service for high-risk women. SAMPLE: 97 high-risk women with a 1.66% or greater five-year risk of breast cancer, atypical hyperplasia, lobular carcinoma in situ, or positive genetic screen. METHODS: A self-assessment questionnaire completed previsit and a satisfaction survey completed postvisit. MAIN RESEARCH VARIABLES: Women's perceived informational, emotional, and decisional support needs, current self-care practices, and satisfaction with the service provided. FINDINGS: Women under age 50 (n = 54) wanted information on breast cancer screening, risk of breast cancer, lifestyle options to lower risk, and hormone replacement therapy; older women (n = 43) wanted information on risk of breast cancer, lifestyle options, breast cancer screening, and chemoprevention. More than 75% of all women wanted information to help them make decisions on breast cancer prevention options, benefits, and risks. The satisfaction survey (N = 61) revealed that most women's needs were met. CONCLUSIONS: Support needs were consistent with the literature that focused primarily on younger women seeking genetic counseling. Proactive planning assisted with addressing the needs of these women. IMPLICATIONS FOR NURSING: A previsit questionnaire facilitates individualized proactive planning before the visit. However, further assessment of self-care practices and emotional needs is required. Interventions should evaluate outcomes, such as accurate risk perception, lifestyle changes, screening follow-through, and decision quality. Advanced practice nurses require specialized skills, including evidence-based risk communication, behavior modification, and decision support.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".