Determining Student Food Insecurity at Memorial University of Newfoundland
Bibliographic record
Abstract
PURPOSE: Our study compared the prevalence of food insecurity among 3 student groups attending Memorial University of Newfoundland (MUN): International (INT), Canadian out-of-province (OOP), and Newfoundland and Labrador (NL). Factors associated with food insecurity were also investigated. METHODS: Data were collected via an online survey distributed to an estimated 10 400 returning MUN students registered at a campus in St. John's, NL. Respondents were recruited through e-mails, posters, and social media. Ten questions from the Canadian Household Food Security Survey Module adult scale were asked to assess food security. Logistic regression was used to compare rates of food insecurity between the three population subgroups. RESULTS: Of the 971 eligible student respondents, 39.9% were food insecure (moderate or severe). After controlling for program type, parental status, living arrangement, and primary income source, OOP and INT students were 1.63 (95% CI = 1.11-2.40) and 3.04 (95% CI = 1.89-4.88) times more likely, respectively, to be food insecure than NL students. CONCLUSIONS: Approximately 40% of participating MUN students experienced food insecurity, a higher proportion than reported for the overall provincial population. Groups at high risk of food insecurity include INT students, students with children, and those relying on government funding as their primary income.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".