Exploring reasons for high levels of food insecurity and low fruit and vegetable consumption among university students post-COVID-19
Bibliographic record
Abstract
High rates of food insecurity and low consumption of fruit and vegetables among university students have been observed prior to the COVID-19 pandemic and intensified during the pandemic. This study aimed to investigate food insecurity among university students and its associations with sociodemographic factors, fruit and vegetable consumption behaviours, and preferred campus programs to address these issues. A convenience sample of 237 Australian university students completed a cross-sectional online survey from October to December 2022. Food insecurity was assessed using the 10-item US Adult Food Security Module, applying the Canadian classification scheme. Sociodemographic variables, fruit and vegetable consumption behaviours, and perceptions of fruit and vegetable access and their affordability were included in the survey. Students were also asked to select the most suitable program(s) and provide reasons for their choice using open-ended questions. Approximately half of respondents (46.4%) were identified as food insecure. The proportion of students meeting the recommended intake of vegetables as specified in the Australian Dietary Guidelines was very low (5.1%) compared with fruit (46.2%). Low fruit consumption was significantly associated with food insecurity (OR = 1.81; 95%CI 1.03, 3.18, p = 0.038). Factors such as the perceived lower accessibility and higher price of fruit and vegetables were significantly associated with higher odds of food insecurity. In terms of potential programs, a free fruit and vegetable campaign was the most popular program, with affordability and physical access being the most frequently cited reasons. These findings suggest that food insecurity is associated with low fruit and vegetable consumption in university students. Therefore, transforming campus food environments and developing food policies at the university level must be considered to address food and nutrition security in university students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".