Recruitment of Males from Underserved Communities in Prostate Cancer Research: Reflections and Recommendations
Bibliographic record
Abstract
Background: Prostate cancer carries a significantly higher burden among Black versus White men in the United States. In Suffolk County, NY, prostate cancer incidence rates are 75% higher among Black individuals with twice the mortality rate. Methods: To address this disparity, a community-based project was implemented. The project used focus groups to assess knowledge, beliefs, attitudes, and health care-seeking practices of Black men. Recruitment was difficult, community members were resistant to participate. Trusted community leaders proved to be the most successful asset for recruitment. Results: In total, three focus groups were held, with a total of 18 participants. Most participants were referred by their school district, pastor, or a community-based organization. Conclusion: A significant lesson learned was the importance of establishing trust, as research done without establishing meaningful connections leads to more mistrust. The downstream effects from the team's initial outreach demonstrate how rich and robust interconnecting relationships may develop within the community.
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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 | 0.107 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".