Conceptualizing the Commercialization of Human Milk: A Concept Analysis
Bibliographic record
Abstract
BACKGROUND: Donor human milk is recommended when infants are unable to be fed their mother's own milk or require supplementation. For-profit companies use technologies to create human milk products for infants in the neonatal intensive care setting without consistent guidelines and regulatory frameworks in place. This commercialization of human milk is inadequately conceptualized and ill-defined. RESEARCH AIMS: The aim of this study is to conceptualize and define the commercialization of human milk and discuss the need for policy guidelines and regulations. METHOD: Using a concept analysis framework, we reviewed the literature on the commercialization of human milk, analyzed the antecedents and potential consequences of the industry, and developed a conceptual definition. The literature review resulted in 13 relevant articles. RESULTS: There has been a surge in the development and availability of human milk products for vulnerable infants developed by for-profit companies. Commercialized human milk can be defined as the packaging and sale of human milk and human milk components for financial gain. Factors contributing to the commercialization of human milk include an increased demand for human milk, and consequences include potential undermining of breastfeeding. The lack of guidelines and regulations raises concerns of equity, ethics, and safety. CONCLUSION: The industry is rapidly growing, resulting in an urgent need for consistent guidelines and regulatory frameworks. If left unaddressed, there could be potential risks for donor milk banking, the future of breastfeeding, and infant and maternal health.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".