ArticleJournal of health care for the poor and underserved2012
Individual breast cancer risk assessment in underserved populations: integrating empirical bioethics and health disparities research.
Article in Journal of health care for the poor and underserved, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 14 citations in OpenAlex.
- "It's Not Just Cost": Barriers to Mammography Screening Access Among Low-income Women in the United States-A Scoping Review.Journal of cancer prevention · 2026Review
- Barriers to Implementation of Breast Cancer Risk Assessment: The Health Care Team Perspective.Journal of the American College of Radiology : JACR · 2023Article
- Perceptions of Breast Cancer Risks Among Women Receiving Mammograph Screening.JAMA network open · 2023Article
- Engaging Women with Limited Health Literacy in Mammography Decision-Making: Perspectives of Patients and Primary Care Providers.Journal of general internal medicine · 2021Article
- Organizational readiness to implement population-based screening and genetic service delivery for hereditary cancer prevention and control.Journal of genetic counseling · 2020Article
- Acceptability of an Interactive Computer-Animated Agent to Promote Patient-Provider Communication About Breast Density: a Mixed Method Pilot Study.Journal of general internal medicine · 2020Article
- A Qualitative Study of Spanish-Speakers' Experience with Dense Breast Notifications in a Massachusetts Safety-Net Hospital.Journal of general internal medicine · 2019Article
- Views of Low-Income Women of Color at Increased Risk for Breast Cancer.Narrative inquiry in bioethics · 2018Article
- Breast Cancer Risk Assessment Among Low-Income Women of Color in Primary Care: A Pilot Study.Journal of oncology practice · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Research suggests that individual breast cancer risk assessment may improve adherence to recommended screening and prevention guidelines, thereby decreasing morbidity and mortality. Further research on the use of risk assessment models in underserved minority populations is critical to informing national public health efforts to eliminate breast cancer disparities. However, implementing individual breast cancer risk assessment in underserved patient populations raises particular ethical issues that require further examination. After reviewing these issues, we will discuss how empirical bioethics research can be integrated with health disparities research to inform the translation of research findings. Our in-progress National Cancer Institute (NCI) funded study, How Do Underserved Minority Women Think About Breast Cancer?, conducted in the context of a larger study on individual breast cancer risk assessment, is presented as a model.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.