Supportive Care Needs of Patients with Breast Cancer Who Self-Identify as Black: An Integrative Review
Bibliographic record
Abstract
Black-identifying patients face many barriers to the receipt of equitable breast cancer care; however, little is currently known about the unique needs of this patient population, particularly in Canada. To address this gap, we identified and thematically grouped constructs from the published literature reporting on the needs of Black-identifying patients with breast cancer and compared these findings to a list generated through a virtual nominal consensus group (NG) attended by Canadian patients with breast cancer who self-identified as Black (n = 3). A scoping review was undertaken, and relevant citations published from database inception until January 2025 were identified from MEDLINE, Embase, and CINAHL. The literature review yielded 34 articles from the United States and identified 15 constructs consistent with the NG, which spanned the cancer continuum from screening to survivorship. The NG identified four additional constructs that were not found in the literature: advocacy and outreach, communication and health literacy, comorbidities and personalized care, and end-of-life care. The final set of constructs was then validated and prioritized by an expert panel consisting of patients with lived experience and relevant community partner organizations (n = 9) to drive future research, advocacy, and policy work. Patient navigation was identified as the top need, with financial support, access to culturally tailored information and resources, culturally relevant care, racialized data for treatment decision-making, and emotional support identified as high-priority needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".