Breast cancer mortality disparities: Providers' perspective
Bibliographic record
Abstract
This research is part two of a study to gain understanding of reasons for the large breast cancer mortality disparity between African-American and White women who live in Memphis, Tennessee. Among the country’s 25 largest cities, the breast cancer mortality disparity is highest in Memphis, Tennessee, where African-American women are twice as likely to die from breast cancer as White women. In part one of this study, we sought to gain the perspective of African-American breast cancer survivors. Now we explore the perspective from the providers of care who interface with breast cancer patients, health systems, and health insurers. This is a descriptive research study that used qualitative methodology to inteview seven medical, surgical and radiation oncologists who serve African-American breast cancer patients in Memphis. Data were collected using semi-structured in-depth interviews. Themes included: (1) socioeconomic factors; (2) lack of knowledge about treatment, progression and side effects, and diagnosis; (3) information/communication about the diagnosis; (4) support system: need for another person to process information given; (5) limited access and resources: no insurance and no available services for treatment in African-American neighborhoods; and (6) fear of the unknown: fear of cancer, fear of losing breast, and fear about the disease’s impact on personal relationships. These results suggest that resources that aid geographical access to services need to change in order for disparities to decrease. A new model for health care delivery for African-American women at high risk of or diagnosed with breast cancer needs to be developed to address these findings.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".