The Hunger to Belong: Student Identity and Belonging Mediate the Relationship Between Social Class and Food Insecurity Among University Students
Bibliographic record
Abstract
ABSTRACT Despite growing awareness and efforts to improve adequate food access, the prevalence of food insecurity among university students continues to rise. Research has consistently demonstrated that food insecurity is more likely to be experienced by students facing immediate financial hardship. However, to date, little research has investigated students' social class background and associated lack of psychosocial resources as risk factors for food insecurity. Recognizing the exclusionary nature of universities for students from lower‐class backgrounds, we propose a novel social identity‐based approach to understanding food insecurity experiences at university. We conducted two cross‐sectional online surveys in 2020 with convenience samples of Australian students (Study 1 N = 2,666; Study 2 N = 177) to explore student identity and sense of belonging to university as mediators of the relationship between social class and food insecurity. In both studies, we found that lower social class was associated with increased risk for experiencing food insecurity, and that this relationship was serially mediated through reduced student identity and belonging. In Study 2, over half of students were experiencing food insecurity, yet fewer than a quarter had accessed any campus support services. Study 2 also found the serial mediation via identification and belonging was contingent on controlling for COVID‐19's impact on students. These findings highlight the importance of student identity and belonging as key drivers of food (in)security that might be harnessed to improve university student outcomes. We suggest that beyond financial and material support, universities must cultivate inclusive environments where students feel psychologically connected, else they are unlikely to utilize such supports even when needed.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.007 | 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".