How do Student Clients of a University-based Food Bank Cope with Food Insecurity?
Bibliographic record
Abstract
PURPOSE: To describe the food security status, food insecurity coping strategies, characteristics, and experiences of student clients of the Campus Food Bank (CFB) at the University of Alberta in Edmonton, Alberta, Canada. METHODS: A convenience sample of 58 students completed a survey from April 2013 to April 2014. Food security status was determined using the "Adult Food Security Survey Module". RESULTS: Ninety percent of CFB student clients who participated in this study were food insecure, which included both moderately and severely food insecure groups. The most prevalent coping strategies for food insecurity included applying for a loan or bursary (86.2%), seeking employment or working more hours (84.5%), and purchasing food using a credit card (77.6%). Participants were a diverse mix of students, including graduate students (50.0%), international students (46.6%), and caregivers of children (24.1%). The most common primary sources of income were government student loans (29.3%) and research assistantships (20.7%). Most participants (82.8%) liked the food they received from the food bank. CONCLUSIONS: Food insecurity is highly prevalent among student clients of this university-based food bank. Students used a variety of coping strategies to increase their disposable income, highlighting the need for additional strategies to alleviate food insecurity among vulnerable students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".