Reconfiguring insufficient breast milk as a sociosomatic problem: mothers of premature babies using the kangaroo method in Brazil
Bibliographic record
Abstract
This study focuses on Brazilian mothers who gave birth to premature babies who were discharged from hospital using the Kangaroo Mother Care Method. While mothers left the hospital breastfeeding exclusively, once back at home, they abandoned exclusive breastfeeding because of insufficient breast milk (IBM). In this project we explored how IBM was interpreted by mothers within their social context. Participatory research using the Creative Sensitive Method was done in the homes of mothers with family members and neighbours. We described the conflicting social discourse that influenced the mothers' perception of IBM and explored their sources of distress. At the hospital and Kangaroo ward, mothers considered that clinicians recognized they were experiencing IBM and thus supported them to overcome this problem. Back at home and in their community, other sources of stress generated anxiety such as: the lack of outpatient clinical support, and conflicting local norms to care and feed premature babies. These difficulties combined with economic constraints and discontinuity in models of health care led mothers to lose confidence in their breastfeeding capacity. Mothers, thus, rapidly replaced exclusive breastfeeding by mixed feeding or formula feeding. Our analysis suggests that IBM in our sample was the result of a socio-somatic process. Recommendations are proposed to help overcome IBM and corresponding contextual barriers to exclusive breastfeeding.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".