On increasing the contribution of locally produced fresh foods to school meals in the Caribbean
Bibliographic record
Abstract
Abstract The rising prevalence of childhood overweight and obesity within the Caribbean is a major public health and policy concern because obese children are at risk of developing non-communicable diseases (NCDs) later in life. Throughout the Caribbean Community (CARICOM), children are consuming unhealthy diets, characterized by energy-dense, processed and ultra-processed foods, sugar sweetened beverages, and limited quantities of fruits and vegetables. Community-based school meal programmes (SMPs) have been identified as useful vehicles to address unhealthy eating among children, and “farm-to-school” approaches have the potential to increase the availability of locally grown nutritious produce, while supporting local agriculture and reducing the region’s reliance on food imports. This paper seeks to better understand the barriers to enhancing community-based school feeding value-chains in the CARICOM, by focusing on the Eastern Caribbean Island of Nevis where there is an interest in developing farm-to-school value chains. Using key informant interviews combined with focus groups with actors along the local food value chain, we identify the following barriers to an effective community-based SMP: a lack of communication and an absence of contractual agreements between local farmers and the SMP administration; generally low levels of child acceptance of school meals containing fresh vegetables; and limited intersectoral coordination and collaboration among SMP stakeholders and local farmers. Using social network analysis, we further discuss limitations in group organization and coordination among local farmers and opportunities for SMP improvement. The results point to the need for more integrative public policy development and greater community engagement to coordinate and strengthen the farm-to school approach to school feeding.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".