Development of “Advancing People of Color in Clinical Trials Now!”: Web-Based Randomized Controlled Trial Protocol
Bibliographic record
Abstract
BACKGROUND: Participation in clinical trials among people of color remains low, compared with white subjects. This protocol describes the development of "Advancing People of Color in Clinical Trials Now!" (ACT Now!), a culturally tailored website designed to influence clinical trial decision making among people of color. OBJECTIVE: This cluster randomized study aims to test the efficacy of a culturally tailored website to increase literacy, self-efficacy, and willingness to enroll in clinical trials among people of color. METHODS: ACT Now! is a randomized trial including 2 groups: (1) intervention group (n=50) with access to the culturally tailored website and (2) control group (n=50) exposed to a standard clinical recruitment website. Clinical trial literacy and willingness to enroll in a clinical trial will be measured before and after exposure to the website corresponding to their assigned group (intervention or control). Surveys will be conducted at baseline and during the 1-month postintervention and 3-month follow-up. Website architecture and wireframing will be informed by the literature and experts in the field. Statistical analysis will be conducted using a two-tailed t test, with 80% power, at .05 alpha level, to increase clinical trial literacy, self-efficacy, and willingness to enroll in clinical trials 3 months post intervention. RESULTS: We will design a culturally tailored website that will provide leverage for community stakeholders to influence clinical trial literacy, self-efficacy, and willingness to enroll in clinical trials among racial and ethnic groups. ACT Now! applies a community-based participatory research approach through the use of a community steering committee (CSC). The CSC provides input during the research study conception, development, implementation, and enrollment. CSC relationships help foster trust among communities of color. ACT Now! has the potential to fill a gap in clinical trial enrollment among people of color through an accessible web-based website. This study was funded in July 2017 and obtained institutional review board approval in spring 2017. As of December 2019, we had enrolled 100 participants. Data analyses are expected to be completed by June 2020, and expected results are to be published in fall 2020. CONCLUSIONS: ACT Now! has the potential to fill an important gap in clinical trial enrollment among people of color through an accessible web-based website. TRIAL REGISTRATION: ClinicalTrials.gov NCT03243071; https://clinicaltrials.gov/ct2/show/NCT00102401. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/17589.
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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 | 0.070 | 0.065 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.131 | 0.024 |
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".