The social cost of student food insecurity at an Atlantic Canadian university: exploring the relationship between social support and psychological distress
Bibliographic record
Abstract
With escalating living expenses and tuition fees, food insescurity continues to moderate university student well-being and academic success. In the postsecondary food security literature, studies assessing distinctions among social support subtypes and how they might interact with mental health are absent. Therefore, the primary objectives of the current study were to characterise students classified as food insecure at an Atlantic Canadian university, compare their social support subtype and psychological distress levels with students classified as food secure, and determine whether/how these subtypes predict psychological distress. Using data from the 2023 National College Health Assessment (NCHA-III) survey (n = 1694), 46.6% were deemed food insecure based on the USDA ERS Food Security scale. A series of two-factor chi-square tests revealed that students classified as food insecure were more likely to be male, undergraduate, international, identify as Black or South Asian, and report lower family incomes and poorer grade point averages (GPAs). These students were also less likely to report campus belongingness, reciprocity, and wellness advocacy. Subsequent t-tests revealed that students classified as food insecure scored lower on each Social Provisions Scale subtype (‘attachment’, ‘guidance’, ‘reliable alliance’, ‘social integration’, and ‘reassurance of worth’), and higher in psychological distress. A hierarchical regression analysis revealed ‘reassurance of worth’ to be the lone, significant support subtype predicting lower psychological distress for students classified as food insecure, a finding which suggests that having one’s self-worth validated, affirmed, and fostered by others may help moderate psychological distress, perhaps by counteracting stigma and shame. The implications of this finding are considered.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".