Culturally tailored support to enhance DASH diet adherence in midlife and older African American women
Bibliographic record
Abstract
Objective: This qualitative study examined the cultural food preferences and resources of African American women with self-reported hypertension to guide the development of a culturally tailored Dietary Approaches to Stopping Hypertension (DASH) manual. Methods: Eleven women from a church congregation in the Southwest United States participated in two focus groups. Transcripts were analyzed using content analysis with independent coding and consensus validation. Results: Six themes and four subthemes emerged: (1) providing perspectives on diet among older African American women; (2) understanding awareness and education needs regarding the DASH diet with subthemes of nutrition-skills and literacy-level adaptation; (3) community disparities in access to healthy foods with subthemes of affordability and nutrition and thrifty nutrition; (4) navigating tradition by addressing challenges of southern cooking heritage; (5) exploring the use of diverse home appliances in meal preparation;and (6) enhancing the DASH manual with tailored insights. Conclusions: Findings emphasize the need for culturally tailored, literacy-sensitive, and resource-conscious materials to promote DASH adherence. A tailored manual may reduce structural and cultural barriers, improve dietary practices, and address hypertension disparities among African American women.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".