Herbal food supplements usage awareness of university students: Example of Echinacea and St. John's Wort
Bibliographic record
Abstract
In recent years, usage of food supplements (Fs) has increased in order to maintain healthy living, have well-being, and be protected from the diseases. There are many medicinal plants used as herbal food supplements (HFs). Within the scope of this study, Echinacea and St. John’s Wort were selected among the plants that are frequently encountered. This descriptive, cross-sectional study was conducted through completing a self-administered online questionnaire by health students. The questionnaire was filled out by 211 students studying at the faculties of Medicine, Dentistry, Pharmacy and Health Sciences at Istanbul Medipol University. The greatest participation was achieved with Pharmacy students (47.4%), whereas the lowest participation was reached with Dentistry students (5.7%). The most commonly used products as Fs were vitamin D (21.3%) and multivitamins (16.1%), while the use of HFs was 8.5%. St. John’s Wort is commonly preferred for wound and burn treatment and Echinacea is used to boost immunity. In parallel with this use, St. John’s Wort is preferred as olive oil maceration and Echinacea as herbal infusion. While the use of HFs was 58.3%, that of the Fs were 44.5%. Echinacea use was found to be 14.4% and St. John’s Wort was 31.3%. The relationship between the presence of chronic disease and the use of Fs or HFs was not statistically significant. This study is significant to detect the opinions and knowledge levels of health students about Fs, especially HFs, St. John’s Wort and Echinacea, which are available in the market.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".