“Everybody I Know Is Always Hungry…But Nobody Asks Why”: University Students, Food Insecurity and Mental Health
Bibliographic record
Abstract
Food insecurity is a substantial problem in Canadian university students. Multiple cross-sectional studies suggest that nearly a third of university students across Canada report food insecurity. Yet, little is understood about the experiences of food-insecure students and the impact of their experiences on their mental health. To address this, a multi-method study was conducted using quantitative and qualitative approaches to describe the prevalence, association and experience of food insecurity and mental health in undergraduate students. The current paper reports on the qualitative component, which described the lived experiences of food-insecure students, captured through face-to-face focus group interviews with participants (n = 6). The themes included (1) contributing factors to food insecurity; (2) consequences of food insecurity; and (3) students’ responses/attempts to cope with food insecurity. The findings illuminated student voices, added depth to quantitative results, and made the experience of food insecurity more visible at the undergraduate level. Additional research is needed to understand students’ diverse experiences across the university community and to inform programs to support students.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".