Perceived Versus Actual Risk for Hypertension and Diabetes in the African American Community
Bibliographic record
Abstract
Hypertension and diabetes mellitus are leading health concerns in the United States. Despite a disproportionate burden of both conditions among African Americans, it is estimated that 44% of diabetes cases and one quarter of hypertension cases within this population are undiagnosed. Lack of awareness of the risk of these conditions may hinder preventive efforts and the adoption of positive lifestyle changes. Based on the findings from a pilot study to develop and standardize uniform screening forms for hypertension and diabetes, this article reports on the perceived risk versus actual risk of developing these conditions among primarily African American participants using a community-based screening tool. Each form assessed both perceived and actual risk of diabetes and hypertension, respectively. A total of 265 hypertension and 225 diabetes screening forms were randomly selected from eight sites across the country. The risk perception of the overall study sample was similar to its actual risk for developing either condition. However, a significant proportion of individuals who scored at high risk for diabetes or hypertension were unaware of their risk for these conditions. These results suggest the need for developing culturally relevant interventions, public health education, and policies that address the risk misperceptions among this group.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".