Delivering a “Dose of Hope”: A Faith-Based Program to Increase Older African Americans’ Participation in Clinical Trials
Bibliographic record
Abstract
BACKGROUND: Underrepresentation of older-age racial and ethnic minorities in clinical research is a significant barrier to health in the United States, as it impedes medical research advancement of effective preventive and therapeutic strategies. OBJECTIVE: The objective of the study was to develop and test the feasibility of a community-developed faith-based intervention and evaluate its potential to increase the number of older African Americans in clinical research. METHODS: Using a cluster-randomized design, we worked with six matched churches to enroll at least 210 persons. We provided those in the intervention group churches with three educational sessions on the role of clinical trials in addressing health disparity topics, and those in the comparison group completed surveys at the same timepoints. All persons enrolled in the study received ongoing information via newsletters and direct outreach on an array of clinical studies seeking participants. We evaluated the short-, mid-, and longer-term effects of the interventional program on clinical trial-related outcomes (ie, screening and enrollment). RESULTS: From 2012 to 2013, we enrolled a balanced cohort of 221 persons in the program. At a 3-month follow-up, mean intention to seek information about clinical trials was higher than baseline in both treatment (mu=7.5/10; sigma=3.1) and control arms (mu=6.6/10; sigma=3.3), with the difference more pronounced in the treatment arm. The program demonstrated strong retention at 3-month (95.4%, 211/221) and 6-month timepoints (94.1%, 208/221). CONCLUSIONS: The "Dose of Hope" program addressed an unmet need to reach an often overlooked audience of older African Americans who are members of churches and stimulate their interest in clinical trial participation. The program demonstrated its appeal in the delivery of effective messages and information about health disparities, and the role of clinical research in addressing these challenges.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".