Public policy interventions to mitigate household food insecurity in Canada: a systematic review
Bibliographic record
Abstract
OBJECTIVE: The objective of this systematic review is to synthesise the evidence on public policy interventions and their ability to reduce household food insecurity (HFI) in Canada. DESIGN: Four databases were searched up to October 2023. Only studies that reported on public policy interventions that might reduce HFI were included, regardless of whether that was the primary purpose of the study. Title and abstract screening, full-text screening, data extraction, risk of bias and certainty of the evidence assessments were conducted by two reviewers. RESULTS: Seventeen relevant studies covering three intervention categories were included: income supplementation, housing assistance programmes and food retailer subsidies. Income supplementation had a positive effect on reducing HFI with a moderate to high level of certainty. Housing assistance programmes and food retailer studies may have little to no effect on HFI; however, there is low certainty in the evidence that could change as evidence emerges. CONCLUSION: The evidence suggests that income supplementation likely reduces HFI for low-income Canadians. Many questions remain in terms of how to optimise this intervention and additional high-quality studies are still needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".