American Indian and Alaska Native recruitment strategies for health-related randomized controlled trials: A scoping review
Bibliographic record
Abstract
BACKGROUND: Significant health disparities exist among American Indian and Alaska Natives (AI/ANs), yet AI/ANs are substantially underrepresented within health-related research, including randomized controlled trials (RCTs). Although research has previously charted representation inequities, there is however a gap in the literature documenting best practice for recruitment techniques of AI/ANs into RCTs. Therefore, the aim of this review was to systematically gather and analyze the published literature to identify common strategies for AI/AN participant recruitment for RCTs in the US. METHODS: A scoping review methodology was engaged with a systematic search operationalized within relevant databases to February 19, 2022, with an additional updated search being carried out up until January 1, 2023: PubMed, Embase, Web of Science, PsycINFO, CINAHL, and Google Scholar. A two-stage article review process was engaged with double reviewers using Covidence review software. Content analysis was then carried out within the included articles by two reviewers using NVivo software to identify common categories within the data on the topic area. RESULTS: Our review identified forty-one relevant articles with the main categories of recruitment strategies being: 1) recruitment methods for AI/ANs into RCTs (passive advertising recruitment approaches, individual-level recruitment approaches, relational methods of recruitment); 2) recruitment personnel used within RCTs; and, 3) relevant recruitment setting. The majority of the included studies used a culturally relevant intervention, as well as a community-involved approach to operationalizing the research. CONCLUSION: Increasing AI/AN representation in RCTs is essential for generating evidence-based interventions that effectively address health disparities and improve health outcomes. Researchers and funding agencies should prioritize the engagement, inclusion, and leadership of AI/AN communities throughout the RCT research process. This includes early community involvement in study design, implementation of culturally tailored recruitment strategies, and dissemination of research findings in formats accessible to AI/AN communities.
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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 | 0.189 | 0.500 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.031 | 0.026 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".