Recruiting black Americans in a large cohort study: the Adventist Health Study-2 (AHS-2) design, methods and participant characteristics.
Bibliographic record
Abstract
OBJECTIVE: The goal of the prospective Adventist Health Study-2 (AHS-2) was to examine the relationship between diet and risk of breast, prostate and colon cancers in Black and White participants. This paper describes the study design, recruitment methods, response rates, and characteristics of Blacks in the AHS-2, thus providing insights about effective strategies to recruit Blacks to participate in research studies. DESIGN: We designed a church-based recruitment model and trained local recruiters who used various strategies to recruit participants in their churches. Participants completed a 50-page self-administered dietary and lifestyle questionnaire. PARTICIPANTS: Participants are Black Seventh-day Adventists, aged 30-109 years, and members of 1,209 Black churches throughout the United States and Canada. RESULTS: Approximately 48,328 Blacks from an estimated target group of over 90,000 signed up for the study and 25,087 completed the questionnaire, comprising about 26% of the larger 97,000 AHS-2-member cohort. Participants were diverse in age, geographic location, education, and income. Seventy percent were female with a median age of 59 years. CONCLUSION: In spite of many recruitment challenges and barriers, we successfully recruited a large cohort whose data should provide some answers as to why Blacks have poorer health outcomes than several other ethnic groups, and help explain existing health disparities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".