BLACK WOMEN’S LIVED EXPERIENCE OF BREAST CANCER
Bibliographic record
Abstract
Context: Data, primarily from the United States, indicates that Black women experience delays in breast cancer treatment, receive non-standard care, and have a lower survival rate. Canada is not immune to racial disparities, but race-based health data is not routinely collected. Objectives: To understand the lived experiences of Black women in Canada living with breast cancer. Methods: One-on-one semi-structured qualitative interviews were conducted with 20 women living in Toronto, Ontario who identified as Black/African/Caribbean and who were currently undergoing or had previously undergone treatment for breast cancer. Data was analyzed using an inductive, constant comparative method to derive themes. Results: Several themes were identified including 1) the importance of social support and community; 2) importance of faith and spirituality; 3) cultural considerations; 4) mental health and psychosocial support; 5) body image and intimacy challenges; 6) importance of fertility preservation; 7) financial burden; 8) lack of representation; and 9) mistrust of the healthcare system. The overarching theme was a sense of feeling alone, unseen, and unrepresented. Recommendations include the importance of advocacy, the need for race-based cancer and health data and the need for racially concordant care. Conclusion: Invisibility and anti-Black racism in healthcare settings are unique concerns for Black women with breast cancer in Toronto. Understanding their needs can help to dismantle medical racism and colourblind healthcare. Further research is needed to develop tools to address these inequities and work towards culturally appropriate and safe approaches.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".