Centering Cultural Approaches in Community-Based Participatory Research to Address the Black Maternal Health Crisis
Bibliographic record
Abstract
Introduction: Black women in Georgia are more likely to die from pregnancy-related complications during the postpartum period than other their non-Black counterparts. Developing effective solutions to address this public health crisis requires strengthened partnerships between researchers and community-based organizations, with the voices of Black women being at the center of the research and innovation. This study examined perspectives of community-advisory board members on a community-based participatory research approach that addresses adverse postpartum health outcomes for Black women living in rural Georgia. Methods: This qualitative descriptive study employed semistructured qualitative interviews with members on the community-advisory board and research team (N=16) consisting of all Black women. Thematic analysis was used to analyze data deductively, with codes being categorized into overarching themes. Results: Themes that were elicited from interviews included community; culture; acceptability of the Positive Outcomes, Research Recommendations, Community Collaborations, and Honorable delivery of healthcare research approach; and Black maternal health disparities. Participants voiced the importance of the inclusion of Black cultural aspects within maternal health interventions and the importance of providing Black women with a platform to share their perspectives and experiences. Participants provided insight on promoting Black maternal health equity and reducing power dynamics within research. Conclusions: Centering culture and community and using Black women's experiences to guide the development of research interventions will be critical in developing relevant solutions to address the Black maternal health crisis. Research should be designed with the input of target communities while supporting synergistic relationships between communities and researchers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".