Engaging for equity: Lessons from a cross-sector partnership addressing prostate cancer risk in the black community
Bibliographic record
Abstract
Black men are more likely to develop earlier and more serious diagnoses of prostate cancer compared to other men. Social and biological factors, such as lifestyle and ancestry, play a large role. Prostate cancer awareness and screening programs in areas with a large black population are important to control risk. Toronto, Ontario has the biggest black community in Canada and is an important place to make sure these programs are effective. A group of community members (doctors, researchers, and patients) from Toronto have come together to learn more about the local community needs and ways to improve awareness and community-based screening. This was the first time team members have worked in this way. This group has selected key lessons from their experiences working together. This includes how to build strong relationships with the local community, the importance of challenging assumptions and unconscious bias, and ways to include people with lived experience (patient partners) in research. These lessons can be a guide for other teams new to health equity work. Through this work, the team as developed creative ways to increase representation in research and assess the needs of Black men in local Toronto communities. Most importantly, these lessons should encourage those hesitant to work in areas leading to advancements in health equity. Black men are faced with a higher risk of developing prostate cancer worldwide, including earlier and more aggressive disease. While there are several known social and biological risk factors attributed to these trends, existing prostate cancer guidelines do not explicitly provide guidance for this group of men. A cross-sector partnership in Toronto, Canada has emerged to address the resulting health disparities through research, outreach, and education. The team benefits by having perspectives from clinicians, community partners, patient partners, and researchers and focuses on understanding predisposition to prostate cancer and the role of community screening. Importantly, working together for the purpose of addressing prostate cancer disparities has led to several important lessons that should be shared with others working in this space. These centre on strengthening community partnerships, confronting assumptions held by team members, and strategizing integration of patient partners as people with lived experience in research. This discussion also includes how the team has approached needs assessments for community-directed work as well as increasing representation in research using local cohorts. The lessons shared here are meant to encourage discourse for both cross-sector partnerships in research as well as health equity research and provide an opportunity to share experiences with others.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.010 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".