Dietary food intake patterns among women in rural South Haiti
Bibliographic record
Abstract
We applied a Food Frequency Questionnaire (FFQ) to 153 mothers of children under five in rural South Haiti from June to late July 2007. The FFQ contained 46 items and used a 3 month reference time period. Over the previous 3 months, the majority of women reported consuming fruits (98.7%), rice (98.7%), plantains (94%), pumpkin (78.4%), corn (95.4%), mangoes (96.7%), papaya (78.9%), watermelon (52.3%), sweet potatoes (92.8%), chicken (81.0%), beef (80.4%), local bread (96.7%), salty snacks (85.0%), fish (85.0%), carrots (93.5%), raw milk (83.0%), kola (60.8%), concentrated milk (60.8%), and liver (53.6%). However, the median consumption for most nutrient dense foods was less than 2 times per week. Only corn, plantain, milk, rice, local bread and mangoes were consumed 3 or more times per week. Foods that were infrequently consumed (i.e. median less or equal 2 times a week) were: watermelon, sweet potatoes, papaya, pumpkin, carrots, liver, chicken, beef, fish, salty snacks, concentrated milk and kola. The above results suggest a need for micronutrient enhanced foods in the area to alleviate potential micronutrient deficiencies. We are currently exploring the potential contributions that orange fleshed sweet potatoes can make towards this goal. Funding provided by CIDA through the Centro Internacional de Agricultura Tropical (CIAT).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".