Dietary intake and knowledge of fermented food products among university students
Bibliographic record
Abstract
Aim: This study examined the knowledge and consumption patterns of fermented foods among undergraduate students, comparing those enrolled in Food and Nutrition programs with students from other academic disciplines. Methods: A cross-sectional survey of 328 Canadian university students gathered demographic data and assessed familiarity, understanding, and intake of fermented food products. Results: While 78% of students reported familiarity with fermented foods, only 23% could accurately define fermentation. Students in Food and Nutrition programs demonstrated stronger knowledge, correctly answering 67% of related questions versus 62% in other disciplines (p = 0.07; 90% CI). Consumption rates were high overall, with 96% of participants reporting they consumed fermented foods. Significant differences were found between groups in specific categories: fermented fruits and vegetables (p = 0.02), soybeans (p = 0.002), grains (p = 0.02), and meat products (p = 0.017). Regarding frequency, 36% of students consumed a variety of fermented foods weekly, while 30% reported monthly consumption. Conclusions: Cultural background, taste preference, and educational focus also appear to influence dietary behaviors. Enrollment in a Food and Nutrition program was linked to both higher knowledge and increased intake. To the best of our knowledge, this is the first Canadian study to explore differences in fermented food knowledge and consumption across academic disciplines. These results support the potential of education-based strategies to promote healthier dietary patterns and warrant further research across more diverse populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".