Rural African American Women and Breast Cancer: Social Determinants of Health Shape Ability to Conceptualize Health In the Arkansas Delta
Bibliographic record
Abstract
We know that certain components including demographics, cultural background, lifestyle choices and lack of access to health care contribute strongly to health disparities in rural regions of the United States. This paper explores perceptions of health, the environment, and the relationships between them that impact health disparities in the Arkansas Delta. The social-ecological model provides a conceptual approach to relate social determinants to health disparities. Few US rural health community-based studies have utilized this approach, or engaged ecological theory to explore rural contexts. This exploratory study blended a community-based, qualitative approach with social-ecological theory, to identify potential social determinants of health that impact rural Arkansans. Methods: Qualitative data were gathered with (n=79) women, ranging in age from 18 to 84, who were residents of 3 rural Arkansas Delta communities. Respondents poignantly described issues that affect health disparities in their communities. Conclusions: The study identified potential social determinants of health at multiple ecological levels among rural African American women. It was the social determinants of health and the legacy of segregation, that impacted their ability to conceptualize health in the resource resisted environment. Keywords: African American women, focus groups, social determinants of health, community based participatory research, social ecological model
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".