Food Hubs as a Means to Promote Food Security in Post-Secondary Institutions: A Scoping Review
Bibliographic record
Abstract
An estimated 20 to 50% of post-secondary students experience food insecurity. Students who are food insecure are more likely to have poor health and lower academic performance relative to food secure peers. Food hubs are physical or digital spaces that provide access to food initiatives and wraparound programs such as employment placement or income support are increasingly of interest as a means to respond to food insecurity. We conducted a scoping review to identify best practices and effective approaches to food hubs that promote food security in post-secondary institutions in North America. The Medline, Embase, CAB Direct and Web of Science databases were searched. A total of 4637 articles were identified and screened by two reviewers. Four articles were included. They encompassed a mix of interventions: a campus pantry and garden, a food rescue program, food literacy-based curriculum and a toolkit to support implementation of interventions on campus. The heterogeneity of studies precluded identification of best practices, but positive impacts of all interventions were noted on metrics such as self-efficacy and greater awareness of food insecurity. The gap in evidence on effective approaches that promote campus food security is a critical barrier to development and implementation of interventions, and should be addressed in future studies.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".