Enhancing the care experiences of Black women along the breast cancer journey: Meaningfully engaging breast cancer survivors to co-create a targeted, culturally relevant resource hub
Bibliographic record
Abstract
There is very little tailored and culturally relevant information available for Black women in Canada around breast cancer. For those who are diagnosed, and who undergo their own breast cancer journey, many feel isolated while navigating care programs that centre around whiteness and perpetuate medical and anti-Black racism. Although it is well-documented that Black women in the United States are often diagnosed with more aggressive forms of cancer and at a younger age, the lack of race-based data in the Canadian context makes it difficult to know for certain how women in Canada are affected. In order to provide trusted, reliable and tailored information, The Peter Gilgan Centre for Women’s Cancers at Women’s College Hospital, in partnership with the Olive Branch of Hope, developed a resource hub that was the first of its kind in Canada, and launched during Black Liberation Month in 2022. Presented in the form of a website and disseminated to over 50 cancer centres and hospitals across the country, components of this resource included, a.) a synthesis of all available evidence on breast cancer disparities for Black women in Canada, mapped to actionable steps b.) representative images and videos of Black clinicians explaining concepts in plain language (from risk factors to reconstruction), c.) community resources compiled from the Olive Branch of Hope and d.) a list of relevant research studies and clinical trials. Guided by principles of Black Feminism and Participatory Action Research, this resource was co-created in partnership with four Black women who were breast cancer survivors (‘co-creators’) who channeled their lived experiences into the project direction. This paper aims to highlight our process with co-creators, discuss key reflections to guide future work and highlight the need for ongoing work in this area.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".