The system of risk factors for diseases in valeological disciplines
Bibliographic record
Abstract
Cite in Vancouver style as: Shevchenko AS, Shevchenko VV, Pomogaybo KG, Danylchenko SI, Brown GW, Shumskyi OL, Bilyi YeYe, Zhoga RА, Yushko VI. The system of risk factors for diseases in valeolog-ical disciplines. Inter Collegas. 2025;12(1):48-65. https://doi.org/10.35339/ic.2025.12.1.ssp Archived: https://doi.org/10.5281/zenodo.15178106 Background. With a view of primary prevention of diseases common in the world, medical workers and educators can effectively work together within teaching valeological disciplines at higher education institutions. Medical information should be adapted for non-medical students using the methods of scaling, aggregation, balancing and system analysis. Their application requires studying the content of main topics of valeological disciplines, determining the boundaries of didactic adaptation and testing in different higher education establishments. Adaptation of medical information on risk factors for understanding by non-medical students has not been studied enough. Aim. To study the characteristics and interrelationships of risk factors of socially significant diseases for the didactic adaptation of the content of valeological disciplines. Materials and Methods. The bibliosemantic method, comparative method, and system analysis method were used for the study. The analysis takes into account the experience of primary prevention of diseases and teaching valeological disciplines in higher education institutions in Ukraine and Germany. Results and Conclusions. The system of risk factors for diseases that are responsible for a significant number of deaths in most countries of the world is the subject of study of modern valeological disciplines. Medical research allows us to divide risk factors into controllable, conditionally controllable and uncontrollable. The impact of the first two groups of risk factors on health can be eliminated or significantly weakened by forming non-medical students’ valeological competence and adherence to a healthy lifestyle throughout life. The programs for primary prevention of socially significant diseases should include recommendations for quitting smoking, alcohol abuse, drug use, overeating, overfatigue, hypodynamia, or, conversely, extreme sports, and unprotected sex. Didactic adaptation of complex medical information is needed for valeological education of non-medical students. Its algorithms are analyzed in this article. Keywords: primary prophylaxis, disease prevention, controllable risk factors, healthy lifestyle, valeological competence, didactic adaptation.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".