A Liberation Health Approach to Examining Challenges and Facilitators of Peer-to-Peer Human Milk Sharing
Bibliographic record
Abstract
BACKGROUND: Human milk sharing between peers is a common and growing practice. Although human milk has been unequivocally established as the ideal food source for infants, much stigma surrounds the practice of human milk sharing. Furthermore, there is little research examining peer-to-peer human milk sharing. Research Aim: We used the liberation health social work model to examine the experiences of mothers who have received donated human milk from a peer. Research questions were as follows: (a) What challenges do recipient mothers experience in peer-to-peer human milk sharing? (b) What supports do recipient mothers identify in peer-to-peer human milk sharing? METHODS: Researchers conducted in-depth interviews with mothers ( N = 20) in the United States and Canada who were recipients of peer-to-peer human milk sharing. Researchers independently reviewed transcripts and completed open, axial, and selective coding. The authors discussed conflicts in theme identification until agreement was reached. RESULTS: Challenges to peer-to-peer human milk sharing were (a) substantial effort required to secure human milk; (b) institutional barriers; (c) milk bank specific barriers; and (d) lack of societal awareness and acceptance of human milk sharing. Facilitators included (a) informed decision making and transparency and (b) support from healthcare professionals. CONCLUSION: Despite risks and barriers, participants continued to pursue peer-to-peer human milk sharing. Informed by a liberation health framework, healthcare professionals-rather than universally discouraging human milk sharing between peers-should facilitate open dialogue with parents about the pros and cons of this practice and about screening recommendations to promote safety and mitigate risk.
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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 | 0.071 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.022 | 0.052 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".