Peer-to-Peer Human Milk Sharing: Recipient Mothers' Motivations, Stress, and Postpartum Mental Health
Bibliographic record
Abstract
BACKGROUND: Some mothers who cannot breastfeed-partially or completely-choose to feed their infants human milk donated from a peer. Few studies have examined mothers' experiences with using donor milk; none has examined whether or not mothers' stress and mental health are associated with using donor milk from a peer. METHODS: Researchers conducted semistructured individual interviews with mothers from the United States and Canada (N = 20) to answer the following questions: (a) what are recipient mothers' motivations for participation in peer-to-peer breast milk sharing and (b) what is the relationship between receiving donated milk and mothers' stress and mental health postpartum? Transcripts were coded using an inductive approach and principles of grounded theory were used to analyze data. RESULTS: Data were organized under two themes: (a) motivations for using milk from a peer and (b) milk-sharing and stress-related experiences. Motivations included health benefits, medical need, and preference for human milk over formula. Factors inducing stress were as follows: logistical stressors of securing donor milk and fear of running out of milk. Factors reducing stress were as follows: donor milk provided relief and comfort and its use reduced mothers' self-reported symptoms of postpartum depression and anxiety. CONCLUSIONS: Mothers participated in peer-to-peer breast milk sharing primarily because of health benefits for children. However, participation also had important psychological benefits for some mothers. Additional research and open discourse are needed to support mothers who choose to use milk from a peer and to promote safety of this practice.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".