Cross‐Nursing in a Peruvian Peri‐urban Area
Bibliographic record
Abstract
Despite recommendations that mothers should avoid the cross‐nursing of their infants (the occasional nursing by another woman) due to the risk of transmitting diseases such as HIV, this practice occurs. Methods A cohort of 179 pregnant women and their toddlers living in a peri‐urban area of Lima, Peru was enrolled during the 3 rd trimester of pregnancy. After birth, a total of 179 newborn infants were followed up until 6 months of age. Field workers visited the homes weekly to collect information about the infant for the previous seven days. Mothers were asked about the infant's health and feeding practices (breastfeeding, use of any liquids or foods, type of food). Results About 45% of the mothers (n=81) reported that their infants received breast milk from another woman during their first 6 months of life. Of those who were cross‐nursed, the frequency of days exposed ranged from one day (35%), between 2 to 7 days (40%), and 8 to 31 days (25%). The exposure occurred as early as the second day of life and as late as the six‐month birthday. Infants were most frequently cross‐nursed by their aunt (69%), followed by their neighbor (11%) and great aunts (11%). Grandmothers were also reported as breastfeeding the infant. Conclusions Although this study provided data about one specific area of Peru, the population included migrants from other areas of the country; it is likely that this feeding behavior is common throughout Peru. More information is required to understand the practice of cross‐nursing and develop training to help health professionals effectively counsel mothers about the risks of this infant feeding practice. Support or Funding Information Canadian Institutes of Health Research
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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 | 0.001 | 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".