Cardiovascular Risk and Its Associated Factors in Health Care Workers in Colombia: A Study Protocol
Bibliographic record
Abstract
BACKGROUND: Cardiovascular diseases are the leading cause of mortality worldwide, for this reason, they are a public health problem. In Colombia, cardiovascular diseases are the main cause of mortality, having a death rate of 152 deaths per 100,000 population. There are 80% of these cardiovascular events that are considered avoidable. OBJECTIVE: The objective of the study is to determine the prevalence of the cardiovascular risk and its associated factors among the institution's workers in order to design and implement interventions in the work environment which may achieve a decrease in such risk. METHODS: An analytical cross-sectional study was designed to determine the cardiovascular risk and its associated factors among workers of a high complexity health care institution. A self-applied survey will be conducted considering sociodemographic aspects, physical activity, diet, alcohol consumption, smoking, level of perceived stress, and personal and family history. In a second appointment, a physical examination will be made, as well as anthropometric measurements and blood pressure determination. Also, blood samples for evaluating total and high density lipoprotein cholesterol, triglycerides, and fasting blood sugar will be taken. A ten-year global risk for cardiovascular disease will be determined using the Framingham score. A descriptive analysis of the population's characteristics and a stratified analysis by sex, age, and occupation will be made. Bivariate and multivariate analysis will be made using logistic regression models to evaluate the association between cardiovascular risk and the independent variables. The research protocol was approved by the Scientific and Technical Committee and the Ethics Committee on Research of the Fundación Cardiovascular de Colombia. RESULTS: The protocol has already received funding and the enrollment phase will begin in the coming months. CONCLUSIONS: The results of this study will give the foundation for the design, implementation, and evaluation of a program based on promoting healthy lifestyles, such as performing regular physical activity and healthy food intake in order to avoid and/or control the cardiovascular risk in the workers of a high complexity health care institution.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".