Evaluation of a traditional food for health intervention in Pohnpei, Federated States of Micronesia
Bibliographic record
Abstract
Federated States of Micronesia (FSM) faces increasing rates of non-communicable diseases related to the neglect of the traditional food system and the shift to consumption of imported food and adoption of sedentary lifestyles. To reverse this trend, a two-year, food-based intervention in one Pohnpeian community in FSM promoted local food production and consumption using a variety of approaches including education, training, agriculture and social marketing following a "Go Local" message. Foods promoted were banana, giant swamp taro, breadfruit and pandanus varieties, green leafy vegetables and fruits for their provitamin A and total carotenoids, vitamins, minerals and fiber content. An evaluation was conducted in a random sample of households (n=47) to examine the extent of dietary changes following the intervention. Results indicated increased (110%) provitamin A carotenoid intake; increased frequency of consumption of local banana (53%), giant swamp taro (475%), and local vegetables (130%); and increased dietary diversity from local food. Exposure to intervention activities was high and there were positive changes in attitudes towards local food. The intervention approaches appear to have been successful in this short period. It is likely that similar approaches in additional communities in Pohnpei and other parts of the Pacific would also be successful in promoting local food. Evidence gathering should continue to document the long-term health outcomes of increased reliance on local food.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".