Rural Community Engagement for Health Disparities Research: The Unique Role of Historically Black Colleges and Universities (HBCUs)
Bibliographic record
Abstract
Reducing health disparities in rural communities of color remains a national concern. Efforts to reduce health disparities often center on community engagement, which is historically the strategy used to provide rural minority populations with support to access and utilize health information and services. Historically Black Colleges and Universities (HBCUs), with their origins derived from social injustices and discrimination, are uniquely positioned to conduct this type of engagement. We present the "Research with Care" project, a long-standing positive working relationship between North Carolina Central University (NCCU) and rural Halifax County, North Carolina, demonstrating an effective campus-community partnership. The importance of readiness to implement Community-based Participatory Research (CBPR) principles is underscored. As demonstrated by the NCCU-Halifax partnership, we recommend leveraging the positive associations of the HBCU brand identity as a method of building and sustaining meaningful relationships with rural Black communities. This underscores the role and value of HBCUs in the health disparities research arena and should be communicated and embraced.
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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 | 0.026 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| 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; 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".