Integrating nutrition outcomes into agriculture development for impact at scale: Highlights from the Canadian International Food Security Research Fund
Bibliographic record
Abstract
The Canadian International Food Security Research Fund programme supported research and scaling up of nutrition- and gender-sensitive agriculture innovations from 2009 to 2018. Women and girls were identified as agents of change and were targeted as the main programme beneficiaries. Projects were implemented in 25 countries through multistakeholder partnerships among universities, research institutions, public and private sectors, and civil society groups, reaching over 78 million people, mainly women and children. Approaches specific to nutrition included growing more nutritious crops, improving dietary diversity, value added processing, food fortification, and nutrition education. Scale-up for impact was achieved through a number of pathways that started with evidence through rigorous research, followed by a combination of elements such as understanding local and regional contexts to identify specific bottlenecks and opportunities for the deployment and adoption of successful innovations, selecting politically effective or influential partners to lead the scaling up process, and investing in long-term local capacity and leadership building. Overall, the knowledge generated in the programme indicate that well-designed nutrition-sensitive agriculture and food-based interventions can have meaningful impacts on pathways that will lead to better health and well-being of women and children through improving household and individual access to nutrient-rich foods. Longer intervention times are needed to demonstrate changes in health indicators such as reduced stunting. This overview paper summarises the programme and showcases examples from studies that demonstrate the impact pathway for nutrition interventions that encompass efficacy and effectiveness studies, value-added processing, cost effectiveness of interventions, and bringing a proven intervention to scale.
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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 | 0.065 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".