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Record W2791098455 · doi:10.1111/apa.14256

The role of breastfeeding in the association between maternal and infant cortisol attunement in the first postpartum year

2018· article· en· W2791098455 on OpenAlexafffund
Wibke Jonas, Rossana Bisceglia, Michael J. Meaney, Aya Dudin, Alison S. Fleming, Meir Steiner

Bibliographic record

VenueActa Paediatrica · 2018
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsMcGill UniversityDouglas Mental Health University InstituteSt. Joseph’s Healthcare HamiltonMcMaster UniversityUniversity of Toronto
FundersCanadian Institutes of Health ResearchForskningsrådet för Arbetsliv och Socialvetenskap
KeywordsBreastfeedingMedicineAttunementPregnancyObstetricsPostpartum periodChildbirthHydrocortisonePediatricsEndocrinology

Abstract

fetched live from OpenAlex

AIM: To explore the role of breastfeeding as a possible link between maternal and infant cortisol attunement across the first postpartum year. METHODS: Mothers (n = 93) provided salivary samples for cortisol levels over a two-day period during mid-pregnancy and at three, six and 12 months and infants at six and 12 months postpartum. Breastfeeding status was established at these same time points. RESULTS: Among breastfeeding mothers, positive correlations were found between maternal cortisol levels during pregnancy and at three months postpartum and infant cortisol at six or 12 months postpartum. Among nonbreastfeeding mothers, these same maternal and infant cortisol relations were inverse and less pronounced. Further, in breastfeeding mothers, the relationship between maternal prenatal cortisol and infant cortisol at 12 months was mediated through maternal cortisol at three months postpartum. CONCLUSION: These results suggest that maternal cortisol levels are positively associated with cortisol levels of the infant, among mothers who breastfeed. This relationship persists over a one-year period.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.184

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.255
Teacher spread0.247 · 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 designObservational
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

Citations16
Published2018
Admission routes2
Has abstractyes

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