Bibliographic record
Abstract
Multiple studies have documented health and healthcare disparities between African Americans and whites in the United States. Many studies have traced these disparities to socioeconomic barriers such as age, income, and level of education. However, it has been found that when variables such as income, access, and insurance are controlled for, health and healthcare disparities remain. A growing body of literature suggests African Americans possess certain health beliefs and perceptions regarding concepts of health, illness, and the healthcare system that influence health and health seeking behaviors. Using empirical generalizations and theory from medical anthropology, this study expands on this growing body of literature by investigating health seeking behavior among African American adults in rural eastern North Carolina, as well as exploring African Americans' perceptions and health beliefs to see how they relate to health seeking behavior. Interviews were conducted with 20 African Americans in two rural eastern counties in North Carolina (Halifax County and Northampton County). Through data analysis, I identified a pattern of health seeking behavior. In addition, thematic analysis revealed that African Americans possess certain health beliefs (e.g. The Body Will Heal Itself) and negative perceptions of the healthcare system (e.g. African Americans do not receive equal treatment), which also influences health seeking behavior. These perceptions and beliefs influenced the timing and decision to seek care. Ultimately, this research sheds light on several factors influential in African Americans' health behaviors that may exacerbate racial disparities in health and healthcare. Consequently, health professionals and policy makers should develop and apply individually appropriate and culturally sensitive policies and interventions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".