The Prevalence of Food Insecurity among University of Guelph-Humber Kinesiology Students
Bibliographic record
Abstract
Food insecurity is defined as inadequate access to sufficient and nourishing food (Food and Agriculture Organization [FAO], 2023). Those aged 15-24, particularly post-secondary students may have a higher vulnerability to developing food insecurity (Bhawra et al., 2021). Commuting costs and on-campus food expenses may elevate the risk among undergraduates. The purpose of this study was to measure the prevalence of food insecurity among UofGH Kinesiology students and to explore potential risk factors. We hypothesized that students living apart from their parents or primary caregivers would be more food insecure. Moreover, we predicted that many factors would contribute to higher levels of food insecurity such as employment status. Participants completed a survey consisting of socio-demographic questions and the 2021 Student Food Experience Survey (Maguire et al., 2021) to assess food insecurity. Results categorized students as secure, moderately insecure, and severely insecure. Their responses were analyzed to determine the relationship between socio-demographic indicators and food insecurity. In both the 2022-2023 school year and the Fall 2023 semester, 20% of students were moderately or severely food insecure. The results also demonstrated that being a non-first-generation student, having to borrow money for food, and perceiving that their academics were impacted by lack of access to food, were predictors of food insecurity. This study demonstrates a 20% prevalence of food insecurity and highlights various contributing. The high prevalence of food insecurity shows the need for future research and interventions at the university level to improve the health and wellness of 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".