Association between Cardiovascular Disease Risk Factor Knowledge and Lifestyle
Bibliographic record
Abstract
Objective: To relate cardiovascular risk factor knowledge to lifestyle. Methods: In this cross-sectional study, food consumption and lifestyle characteristics were recorded using mailed questionnaires. The dietary pattern was described using the Mediterranean Diet Score (MDS). An open ended questionnaire without predefined choices or answers was used to capture cardiovascular knowledge. Results: Lack of physical activity, smoking and eating too much fat were the 3 most cited potential cardiovascular risk factors, while being overweight, eating too much salt and a low consumption of fruits and vegetables were the least cited risk factors. Age, Body Mass Index, physical activity, smoking, income and dietary habits were not consistently associated with knowledge of risk factors. A low socioeconomic position as measured by the indicator education was associated with a lower knowledge of established and modifiable cardiovascular risk factors. Conclusions: Risk factor knowledge, an essential step in prevention of CVD, is not systematically associated with a healthier lifestyle. The findings of this study confirm that there is a gap between risk factor knowledge and lifestyle.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".