Relationship Between Cardiovascular Disease Knowledge and Race/Ethnicity, Education, and Weight Status
Bibliographic record
Abstract
BACKGROUND: Inadequate cardiovascular disease (CVD) knowledge has been cited to account for the imperfect decline in CVD among women over the last 2 decades. HYPOTHESIS: Due to concerns that at-risk women might not know the leading cause of death or symptoms of a heart attack, our goal was to assess the relationship between CVD knowledge race/ethnicity, education, and body mass index (BMI). METHODS: Using a structured questionnaire, CVD knowledge, socio-demographics, risk factors, and BMI were evaluated in 681 women. RESULTS: Participants included Hispanic, 42.1% (n = 287); non-Hispanic white (NHW), 40.2% (n = 274); non-Hispanic black (NHB), 7.3% (n = 50); and Asian/Pacific Islander (A/PI), 8.7% (n = 59). Average BMI was 26.3 ± 6.1 kg/m(2) . Hypertension was more frequent among overweight (45%) and obese (62%) than normal weight (24%) (P < 0.0001), elevated total cholesterol was more frequent among overweight (41%) and obese (44%) than normal weight (30%) (P < 0.05 and P < 0.01, respectively), and diabetes was more frequent among obese (25%) than normal weight (5%) (P < 0.0001). Knowledge of the leading cause of death and symptoms of a heart attack varied by race/ethnicity and education (P < 0.001) but not BMI. Concerning the leading cause of death among women in the United States, 87.6% (240/274) NHW answered correctly compared to 64% (32/50) NHB (P < 0.05), 28.3% (80/283) Hispanic (P < 0.0001), and 55.9% (33/59) A/PI (P < 0.001). Among participants with ≤12 years of education, 21.2% knew the leading cause of death and 49.3% knew heart attack symptoms vs 75.7% and 75.5%, respectively, for >12 years (both P < 0.0001). CONCLUSIONS: Effective prevention strategies for at-risk populations need to escalate CVD knowledge and awareness among the undereducated and minority women.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".