The role of traditional foods for infants and young children in the Peruvian Amazon
Bibliographic record
Abstract
Rationale. To understand the role of traditional foods in dietary quality and child growth, Awajún infant and young child feeding practices and nutritional status must be established. Methods. Research took place in 6 Awajún communities, Cenepa, Peru. Mothers completed repeat dietary recalls and infant feeding histories for their children (n= 32, 0–25 mo). Adequacy of complementary foods was compared to WHO guidelines. Anthropometry was measured to estimate nutritional status. Results. Based on dietary data some infants received adequate nutrition from breastfeeding and complementary foods; however, feeding practices varied. Complementary feeding from 12–23 months was nearer nutrient intake recommendations than for 6–11 months. Purchased or donated foods were not as nutrient dense as traditional foods. Overall, 39.4% of the children were stunted (HAZ <−2), with mean HAZ −1.71 ± SD 1.41. Nutritional status declined with increasing age. Conclusions. Several mothers practiced exclusive breastfeeding and appropriate complementary feeding with Awajún traditional foods; however, given the range of feeding practices, introduction of market foods and inadequate nutritional status of children, health promotion for infant and young child feeding is warranted. Funding: Canadian Institutes for Health Research (CIHR), Institutes of Aboriginal Peoples Health (IAPH) & Population and Public Health (IPPH)
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".