Local concepts of infant illness among Mam‐Mayan women and impact on feeding practices: a qualitative study in the Western Highlands of Guatemala
Bibliographic record
Abstract
Objectives To examine maternal beliefs regarding etiology, presentation and treatment of prevalent child illnesses in rural Mam‐Mayan Guatemalan communities. Methods 9 pregnant and 9 early‐lactating women already enrolled in a concurrent maternal‐infant health study participated in semi‐structured, in‐depth interviews. Two focus groups were conducted with community women. All interviews were audio recorded, transcribed and analyzed with HyperResearch. Findings Most women ascribe infant parasitic, diarrheal and respiratory illnesses to a mix of local/culture‐bound and biomedical concepts of illness. Traditional herbs are routinely used to treat or prevent these conditions. Compliance with food requests made by the infant was mentioned as being protective against parasites, including during the first 6mo of life. Whereas “cold air” was associated with respiratory illness, specific foods considered “cold” in local narratives (fruits, chicken, avocado, potatoes) were associated with gastrointestinal illness. Conclusions Maternal beliefs about childhood illness influence infant feeding practices and may determine both exclusivity of breastfeeding during lactation and infant diet variety. Better comprehension is needed of how socio‐cultural realities impact health‐related behaviors and feeding practice outcomes. Funding: TUSM, IDSA, ASTMH
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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 | 0.002 | 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 | 0.005 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".