Digital Resources Aiming to Address or Alleviate Post-Secondary Food Insecurity: A Scoping Review
Bibliographic record
Abstract
Food insecurity is a significant and growing concern among post-secondary students within Canada. According to The Sandbox Project (2022), nearly 40 percent of Canadian post-secondary students experience some level of food insecurity including skipping meals, reducing their food intake, or going hungry due to financial constraints, or lack of access. The issue of food insecurity disproportionately affects students and has been linked to adverse outcomes such as poor mental and physical health, academic struggles, and increased stress (Payne-Sturges et al., 2018). Despite the growing awareness of student food insecurity, many traditional food assistance programs are not fully accessible to students, leaving gaps in support and highlighting the need for student-centred solutions. The purpose of this scoping review is to systematically map existing evidence on how digital resources such as mobile applications, online platforms, and other tech-enabled initiatives are supporting efforts to decrease food insecurity within Canadian post-secondary students. With increasing integration of digital technology into health and social support systems, understanding the current use of these interventions is important
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".