Improving household nutrition security and public health in the CARICOM, 2018–2022
Bibliographic record
Abstract
The Caribbean is experiencing a worsening epidemic of obesity and noncommunicable diseases (NCDs) and it has the worst rates of premature mortality from cardiovascular diseases in the region of the Americas. Creating enabling environments to improve dietary diversity would help reduce obesity and diet-related NCDs. The Improving Household Nutrition Security and Public Health in the CARICOM project aimed to increase dietary diversity in the Caribbean, and to determine and implement effective, gender-sensitive interventions to improve food sovereignty, household food security, and nutrition in CARICOM states. Primary quantitative and qualitative research, scoping reviews, stakeholder engagement, implementation of interventions and dissemination activities were undertaken. This paper describes the overall project design and implementation, discusses challenges and limitations, and presents core achievements to inform further work in Small Island Developing States throughout CARICOM to advance the nutrition agenda in the Caribbean. The results of the project's research activities are presented in other papers published in this special issue on nutrition security in CARICOM states.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".