[Rational nutrition as a prerequisite for eliminating the risk of overweight: public opinion and practice].
Bibliographic record
Abstract
OBJECTIVE: Introduction: The awareness of the working-age population regarding the principles of a healthy lifestyle and risk factors of diseases is analyzed. Aim: To identify and characterize the level of awareness of the working-age population regarding the essence of a healthy lifestyle and risk factors (with a focus on irrational nutrition and its consequences). PATIENTS AND METHODS: Materials and methods: In the sociological study (2017), on request of Dnipropetrovsk regional state administration, methods of deep (N20) and personalized formal interviews (N2000) were used according to a specially developed methodology of qualitative and quantitative research stages and data collection tools. RESULTS: Results: In their understanding of a healthy lifestyle, 71% of the respondents shared the idea of a mandatory nutrition that meets the needs of a particular person. With a fairly complete awareness as a whole, there is a spread of unhealthy drinking practices with high sugar content (26.7% of respondents), street food (16%), and visiting fast food establishments (10.7%). It has been shown that the rationalization of nutrition at the primary level is underestimated: only 12.7% of the respondents received advice from their family / district doctor or nurse over the past 12 months about the change in nutrition. At the same time, less than a quarter of the recommendations were specified, the rest concerned only healthy eating at all. Therefore, on average, one in three respondents reported adherence to the advised advice. Significantly, more than 40% of respondents consider a significant obstacle to implementing the recommendations of the high cost of "healthy" products. CONCLUSION: Conclusions: Awareness of the population of Dnipropetrovsk region about the danger of inappropriate nutrition is estimated as insufficient. There is a clear need to increase the training of family medicine, medical staff and combine their activities with information and education work among the population to minimize this risk factor, develop motivation to healthy choices in nutrition practices, and use of sociological research as a basis for measures to improve the medical literacy of the population.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".