Intake of vitamin A- and iron- rich foods by young children in a poor peri-urban community in the Dominican Republic.
Bibliographic record
Abstract
Introduction: Vitamin A and iron are two of the most common micronutrient deficiencies in children, both of which may cause adverse outcomes [1-4] . While educational interventions may help, pre-existing dietary patterns are often not obtained prior to such interventions [5] . Examining consumption of locally available vitamin A and iron rich foods (VAIRFs) by children may inform pragmatic tailored health messages. Methods: All caregivers of children under five years of age participating in a community-based growth monitoring service in a poor peri-urban community near Santo Domingo, Dominican Republic, were eligible to participate. At each appointment, participants (n=162) completed structured interviews that included questions on the child’s consumption of VAIRFs in the previous seven days. These data were combined with information elicited from focus group discussions with mothers to generate preliminary health messages. Results: Most of the locally available VAIRFs were consumed less than 30% of the time across age groups, with particularly low values for lentils and spinach. Low frequency was a particular concern for the 6-12 month olds (e.g., carrots and squash were only consumed 18.3% and 28.9% of the time, respectively, vs. 40.7% and 38.5%, respectively, in the 24-60 month olds). Discussion: Examples of preliminary health messages informed by the findings include expansion of use of carrots as juice mixed with other foods as locally practiced, and a squash puree mixed with commonly consumed eggs in the 6-12 month age group. Further study is needed to determine the acceptability and impact of these preliminary health messages.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".