Building Equitable Partnerships Between Industry Sponsors and Indigenous Communities to Enhance Engagement in Clinical Trials
Bibliographic record
Abstract
INTRODUCTION: American Indians, Alaska Natives, and Native Hawaiians (AI/AN/NH) have among the lowest representation in clinical trial participation in the United States (US) compared with other racial/ethnic groups and experience many barriers to health care access. To promote equitable and justice-centered inclusion of Indigenous Peoples in clinical trials and improve health equity, industry sponsors need to be better attuned to community-based priorities. This article summarizes perspectives including strategies to build more effective and equitable partnerships with Indigenous communities in the US and to advance access to medical care. METHODS: A panel of advisors on AI/AN/NH health care assembled for a virtual roundtable discussion in March 2024. A narrative review, supported by key publications, was conducted to summarize and contextualize the discussions. RESULTS: AI/AN/NH face various health inequities and challenges in clinical trial enrollment, including justified distrust of medical research environments, inaccessible and unaffordable health care, and limited community engagement by the research community. Proposed methods for engagement based on advisor insights were developed to guide industry sponsors in building more effective partnerships with Indigenous communities. Engagement methods consist of several strategies such as investing in community priorities, building a long-term commitment, identifying trusted messengers, and codeveloping engagement initiatives. CONCLUSIONS: Current challenges regarding clinical trial diversity are impacting health outcomes among Indigenous Peoples, furthering disparities. Based on advisor engagement, establishing effective, equitable, and justice-centered partnerships between industry sponsors and Indigenous Peoples has the potential to result in community-driven priorities being recognized in clinical trials, and thus expanding benefit of medical innovation.
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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 | 0.193 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".