Feeding practices during pregnancy and lactation amongst Mam‐Mayan women in rural Guatemala: a mixed qualitative and quantitative evaluation
Bibliographic record
Abstract
Objectives To explore local beliefs influencing feeding practices during pregnancy and lactation among Mam‐Mayan women in rural communities in the Western Highlands of Guatemala. Methods 12 pregnant (P), 13 lactating (L) women and 2 midwives participated in semi‐structured interviews. 2 focus groups were also conducted with women. All sessions were audio recorded, transcribed, and analyzed using HyperResearch. A separate survey of 61 P and 67 L women inquired about their food practices. Findings Most P and L women recognize the importance of consuming nutrient‐rich foods so that they may be passed on to the growing infant. Many P women reported decreasing their food intake. 34% of P and 36% of L women reported avoiding certain foods during pregnancy, most commonly animal products (beef and poultry). A majority of L women reported increasing their intake during lactation, while 19% reported avoiding certain foods, especially “cold” foods such as black beans, believed to negatively impact infant health. On the other hand, a majority of L women believe that consuming “hot” foods, such as corn atole , improves milk quantity. Conclusions Increasing general dietary intake, especially protein intake during pregnancy is a key area for action. The hot/cold dichotomy is recognized within the study communities and may play a role in diet choices during pregnancy and lactation. Funding: TUSM
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".