Diet habits of employees in higher education
Bibliographic record
Abstract
Introduction: Inadequate nutrition, excessive use of alcohol and tobacco can significantly increase the risk of chronic mass non-communicable diseases, which are responsible for more than two-thirds of deaths in the adult population globally. The aim of this paper is to analyze the risk factors for chronic non-communicable diseases among employees in higher education. Material and methods: The research was conducted in the form of a cross-sectional study during May 2022. The surveyed population consists of 31 employees of the College of Vocational Studies in Subotica. The research instrument consists of a survey questionnaire created for research purposes. The data analysis was performed using the statistical package SPSS 20. The methods of descriptive statistical analysis, cross-tabulation analysis were applied, and the parametric ch2 test was used to confirm the correlation. Results: A third of the employees have been diagnosed with a chronic disease, most often hypertension and hypercholesterolemia. More than half (58%) of employees eat breakfast, 87% eat lunch and 52% eat dinner regularly. About 60% of employees consume fruits and vegetables at least 5-6 days a week, significantly more often employees over 45. Almost half (42%) of employees consume sweets at least 5-7 days a week, or more often. A quarter (24%) of employees consume alcoholic beverages on a weekly basis. Conclusion: The results of the study show that among employees in higher education there are inadequate eating habits that can increase the risk of chronic mass non-communicable diseases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".