Food Experiences and Dietary Patterns of International Students at a Canadian University
Bibliographic record
Abstract
Purpose: Postsecondary students who move to a new country to continue their education experience a drastic shift in food landscape, often resulting in unhealthy dietary adaptations. Methods: This mixed-method study explored the eating experiences and dietary patterns of 30 international students attending a Canadian university. Data were collected through focus groups that were analyzed thematically and dietary records analyzed for compliance with Canada’s Food Guide for Healthy Living (2007). Results: Results showed that many participants want to maintain home country food traditions, yet their new independent living status and novel food environments led to changes in dietary habits. Limited food skills, busy schedules, reduced access to familiar foods, and fast-food environments contributed to unhealthy eating patterns. These perceptions were reflected in the high consumption of “other”, less nutritious foods and low servings of fruits and vegetables, milk products and alternatives, and grain products. Conclusions: These findings show that international university students face dual challenges of transitioning into independent living without sufficient food skills and family supports, and exposure to unfamiliar and unhealthy food environments. Resources need to be developed for newly enrolled international students that emphasize food awareness, food skills, and healthy eating habits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".