A Behavioral Assessment of College Students’ Knowledge, Awareness, and Consumption on Snack Foods That May Contain Probiotics
Bibliographic record
Abstract
With the increasing variety of snack foods containing probiotics infiltrating the market, it is important that consumers become more aware and knowledgeable about these products. The aim of the current study was to investigate potential consumers’ behavior by assessing knowledge about probiotics, awareness of snack foods containing probiotics, and frequently consumed snacks among student college departments within a university setting. Participants included 125 college students (n = 34 male, n = 91 female), all 18 years and older, and evaluated via a 19-item questionnaire using descriptive statistics, one-way analysis of variance (ANOVA) and Gabriel’s post hoc test. Level of significance was set at p ≤ 0.05. There was a statistically significant difference in knowledge about probiotics among the student college departments, p = 0.012. Specifically, students in the College of Health and Human Services (CHHS) were statistically significantly more knowledgeable than those in the Science, Technology, Engineering and Mathematics (STEM) college, p = 0.010. There was no statistically significant difference in awareness of snack foods containing probiotics, p = 0.262. On average, participants’ knowledge about probiotics was low (48.1%) and awareness of snack foods containing probiotics was very low (2.5%), though, a majority of participants (94.1%) were aware that yogurt may contain probiotics. Overall, these findings should guide food product developers and marketers to create products that are relevant and messages that enhance consumers’ knowledge and awareness to the existence of the probiotics in that product.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".