The seasonality of nutrition status in Shawi Indigenous children in the Peruvian Amazon
Bibliographic record
Abstract
Research on the impact of seasonal and climatic variability on childhood nutritional status in the Amazon is limited. We examined how the nutritional status of Shawi children under five years changed seasonally and explored parental participation in food system activities (fishing, livestock, agriculture, hunting) as a potential influence. Using a community-based research approach with Indigenous Shawi Peoples, we conducted cross-sectional surveys in pre-harvest (July-August 2014) and post-harvest (November-December 2015) seasons. Sociodemographic data, parental participation, weight, height, and hemoglobin concentration were collected for childhood nutritional assessment. We employed bivariable linear regression to analyze associations between seasonal variations in children’s nutrition and parental food system engagement. The study took place across eleven Indigenous Shawi communities in Loreto, Peruvian Amazon. In total, 74 Shawi children and their parents were analyzed. Results indicated a decrease in childhood wasting (4.9% to 0.0%) and persistent anemia (66.2% to 66.2%), while stunting increased (39.2% to 41.9%) from pre-harvest to post-harvest. Parental participation in food activities varied seasonally, but its impact on childhood nutritional status was not statistically significant. Our findings highlight significant levels of undernutrition in Indigenous Shawi children, with slight seasonal variation. Future interventions must consider seasonal dynamics, and further exploration of parental roles in children’s diets is warranted.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".