Infant and young child feeding in the Peruvian Amazon: the need to promote exclusive breastfeeding and nutrient‐dense traditional complementary foods
Bibliographic record
Abstract
The study objective was to understand the role of traditional Awajún foods in dietary quality and the potential impacts on growth of Awajún infants and young children 0-23 months of age. Research took place in April and May of 2004, along the Cenepa River in six Awajún communities. Anthropometry estimated nutritional status for 32 infants (0-23 months). Repeat dietary recalls and infant feeding histories were completed with 32 mothers. Adequacy of the complementary foods was compared with World Health Organization guidelines. Anthropometry indicated a high prevalence of stunting (39.4% of infants and young children), with nutritional status declining with age. Half of the Awajún mothers practised exclusive breastfeeding. Dietary recalls and infant food histories suggested that many of the infants were getting adequate nutrition from complementary foods and breastfeeding; however, there was variation in breastfeeding and complementary feeding practices among the mothers. Complementary feeding for young children 12-23 months generally met nutrient recommendations, but mean intakes for iron, zinc, calcium and vitamin A were inadequate in infants 6-11 months. Traditional foods provided 85% of energy and were more nutrient dense than market foods. Appropriate infant and complementary feeding was found among some women; however, given the range of feeding practices and introduction of market foods, health promotion targeting infant and young child feeding is warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".