The impacts of the Nutrition North Canada program on the accessibility and affordability of perishable, nutritious foods among eligible communities: a scoping review
Bibliographic record
Abstract
The Nutrition North Canada (NNC) program, introduced in April 2011 is a federal strategy to improve access to perishable, nutritious foods for remote and isolated communities in northern Canada by subsidising retailers to provide price reductions at the point of purchase. As of March 2023, 123 communities are eligible for the program. To evaluate existing evidence and research on the NNC program to inform policy decisions to improve the effectiveness of NNC. A scoping review of peer-reviewed articles was conducted in ten databases along with a supplemental grey literature search of government and non-government reports published between 2011 and 2022. The search yielded 172 publications for screening, of which 42 were included in the analysis. Narrative thematic evidence synthesis yielded 104 critiques and 341 recommendations of the NNC program across eight themes. The most-identified recommendations focus on transparency, communication, and support for harvesting, hunting, and community food initiatives. This review highlights recommendations informed by the literature to address critiques of the NNC program to improve food security, increase access to perishable and non-perishable items, and support community-based food initiatives among eligible communities. The review also identifies priority areas for future policy directions such as additional support for education initiatives, communication and transparency amidst program changes, and food price regulations.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".