Knowledge and Perceptions of Carbohydrates among Nutrition-Major and Nutrition-Elective Undergraduate Students in Canada
Bibliographic record
Abstract
Objective: The purpose of the study was to assess knowledge and perceptions related to carbohydrates, including sugars, among Canadian nutrition-major undergraduates compared to those enrolled in elective nutrition courses (i.e., “nutrition-elective students”).Methods: Cross-sectional surveys were distributed during class time at eight Canadian universities, which included 32 questions on demographics, knowledge and perceptions of carbohydrates and sugars. Descriptive analyses were performed. Differences between groups were tested by Chi-squared statistics.Results: A total of 1207 students (60% nutrition-majors) participated in the survey (January 2016–February 2017). Internet-based sources accounted for one-third of the sources where students obtained nutrition information. About 61% of internet-bases sources were “online” or “website” with no qualifiers, and about a quarter was from social media. A higher percentage of nutrition-majors correctly answered knowledge questions of carbohydrates compared with nutrition-elective students (p < 0.01); no difference was observed for sugars-related knowledge questions. The perceptions of sugars were generally negative and did not differ between groups.Conclusions: Several knowledge gaps and common perceptions on topics related to carbohydrates and sugars were identified; nutrition-major students performed better than nutrition-elective students on carbohydrate knowledge questions, but not sugars. These results highlight the importance of identifying methods to help students bridge knowledge gaps and develop skills to critically evaluate nutrition information from various resources and challenge personal biases.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".