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Record W4399500438 · doi:10.1177/08903344241254345

Conceptualizing the Commercialization of Human Milk: A Concept Analysis

2024· review· en· W4399500438 on OpenAlexaff
Heather Christine Rusi, Laurence M. Grummer‐Strawn, Maryanne T. Perrin, Tracie Risling, Meredith Brockway

Bibliographic record

VenueJournal of Human Lactation · 2024
Typereview
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersWorld Health Organization
KeywordsCommercializationBreastfeedingBusinessEquity (law)Profit (economics)MarketingBiotechnologyMedicineEconomicsPolitical sciencePediatrics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.747
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.114
GPT teacher head0.460
Teacher spread0.346 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations8
Published2024
Admission routes1
Has abstractyes

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