A Combined Theory of Change-Group Model Building Approach to Evaluating “Farm to Fork” Models for School Feeding in the Caribbean
Bibliographic record
Abstract
There is a scarcity of research on building nutrition-sensitive value chains (NSVCs) to improve diets and nutrition outcomes of populations in the Caribbean. This study contributes to filling this research gap by outlining a participatory approach to evaluating a NSVC model for “farm to fork” (F2F) school feeding in the Eastern Caribbean Island of St. Kitts. Using a combined group model building (GMB) and theory of change (ToC) approach, policy actors and other stakeholders (n= 37) across the school feeding value chain were guided through a facilitated process to evaluate the ToC underlying a series of F2F interventions designed to enhance childhood nutrition. Stakeholders at the workshop engaged collaboratively to create a causal map of interconnected “system factors” that help explain behaviors contributing to unhealthy eating among children that extended well-beyond the original F2F project ToC that had been used to inform interventions. Through this facilitated GMB process, stakeholders proposed additional food system interventions, and identified multiple “impact pathways” and “mediating influences” underlying local availability and consumption of nutritious foods in local school environments. Workshop participants were also able to identify leverage points where community-level efforts, alongside research interventions, may ensure that initiatives for building local NSVCs are ultimately institutionalized. Results of this study suggest that developing NSVCs for school feeding and food systems in the Caribbean requires both locally driven innovation and the leveraging of system-wide resources, with lessons for project intervention strategies.
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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 | 0.055 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".