Integrating evidence into practice: examples from the Slovenia National Program on CVDs Prevention
Bibliographic record
Abstract
Background The burden of chronic non-communicable diseases (NCDs) represents one of the major challenges of Slovenia's development. Integrated prevention and control of NCDs, including CVDs, is carried out at the primary health care level since 2002 as systematic and universally accessible program. In 2011 it was upgraded to introduce registered nurses who deliver mainly preventive services. Methods Target population of the program is represented by adults above 30 years of age. Key components of the program are screening with assessment of cardiovascular risk, and non-pharmacological support to individuals in changing lifestyle (healthy weight loss, healthy diet, physical activity for health, quit smoking, reduce alcohol consumption and support in dealing with depression) delivered in 61 Health Education Centers. The program was developed based on several different types of scientific evidence and is adhering to current international CVDs prevention guidelines. The interventions to support healthy lifestyles are based on scientific evidence on health benefits of healthy diet and nutrition (low salt, low fat, low sugar), of regular physical activity, of tobacco control and of reducing the harmful use of alcohol. The approaches to support the lifestyles change are based on motivation interview technique Calgary - Cambridge Model and on the Transtheoretical Model of behavior change. Results The register of the program holds over 500.000 entries for the persons screened and treated over more than 10 years. In the last twenty years, in Slovenia the number of deaths from all types of CVDs halved. Compared to year 2000, the number of deaths from these diseases is reduced by around 1,200 annually. Conclusion Significant changes in CVD mortality have been observed, but how much of these are attributable to the preventive program needs to be still determined.
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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 | 0.037 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".