Food Insecurity among College Students
Bibliographic record
Abstract
OBJECTIVE: To assess the prevalence and determinants of food insecurity among college students at the University of Nevada, Las Vegas (UNLV) during the COVID 19 pandemic. DESIGN: Cross-sectional study that collected online survey data from a convenience sample of college students. Setting: UNLV, Las Vegas, Nevada, United States. Participants: 310 UNLV students 18 years of age and older, who were enrolled during the 2020 Fall semester. RESULTS: A total of 29.4% (n=97) of the study participants were food insecure. Students with a household income greater than $50,000 were 81% less likely to be food insecure (P < 0.01) compared to students with a household income lower than $50,000. Students who reported their general health as good, fair, or poor were 2.19 times more likely to be food insecure (P = 0.02) compared to students who reported their general health as excellent or very good. For each increase in GPA of 1 point, the odds of being food insecure decreased by 58% (P = 0.01). CONCLUSIONS: This study highlights the high prevalence of food insecurity among UNLV students and provides public health professionals and policymakers with the scientific basis to develop interventions and policies aimed at reducing the rates of food insecurity among college 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".