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Record W2940797319 · doi:10.3148/cjdpr-2019-006

Acceptability of Donor Human Milk in Muslim Populations in Canada

2019· article· en· W2940797319 on OpenAlexaffvenueabout
Nicole Clouthier, Cindy Ulrich, Brenda Hartman

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2019
Typearticle
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsAnonymityKinshipBreast milkIslamPasteurizationMedicineHuman breast milkBreast feedingFamily medicineDemographyGeographyPediatricsPolitical scienceSociologyBiologyFood scienceLaw

Abstract

fetched live from OpenAlex

Human milk is considered to be the best nutritional option for newborns. When a mother gives birth prematurely, she may have difficulty providing breast milk for her child. Pasteurized donor human milk (DM) is a better feeding alternative than preterm formula. Human milk banks in North America pool the milk from up to 5 women before distribution, a concept which does not pose a problem for most living in the Western world. Muslim families living in North America may reject the use of DM due to the idea of milk kinship and the anonymity of the donor. This paper aims to provide knowledge to Canadian clinicians on these Islamic religious beliefs relevant to DM and how they may impact their practice. Additionally, this paper provides Canadian clinicians with information to alleviate concerns Muslim families may have regarding the use of DM for their preterm infant.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.244

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.002
Scholarly communication0.0020.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.125
GPT teacher head0.435
Teacher spread0.310 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations2
Published2019
Admission routes3
Has abstractyes

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