Food-based interventions to mitigate household food insecurity in Canada: a systematic review
Bibliographic record
Abstract
INTRODUCTION: Household food insecurity (HFI) is a persistent and important public health and policy concern within Canada that continues to be widespread in the face of economic uncertainties and inflation. The objective of this systematic review was to synthesize the evidence on food-based interventions that could reduce HFI in Canada. METHODS: Studies that assessed a food-based intervention that might reduce food insecurity and measured HFI were included, regardless of whether that was the primary purpose of the study. Four databases were searched up to 19 February 2025. Screening of abstracts and full texts, data extraction, assessments of risks of bias and certainty of the evidence were conducted independently by two reviewers. PROSPERO CRD42021254450. RESULTS: Exposure to food voucher programs may reduce HFI, but exposure to food box, community gardening, school food, hunting and fishing, and food charity programs may have little to no effect on HFI. The rate of utilization of food banks by food-insecure households may be low and depends upon food insecurity level and population group. CONCLUSION: Food charities may be a last resort for those in need of short-term access to emergency food (i.e. populations experiencing homelessness). However, given the pervasive nature of HFI as a marker of deprivation, it is unlikely that food-based responses will have a major impact on overall HFI, which is primarily an economic problem. A more comprehensive public policy approach to mitigate HFI is likely required.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".