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Record W39401846

The impact of maternal positive and negative affect on fetal physiology and diurnal patterns.

2014· article· en· W39401846 on OpenAlexaff
Gillian E. Hanley, Dan Rurak, Kenneth Lim, Ursula Brain, Tim F. Oberlander

Bibliographic record

VenuePubMed · 2014
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsChild and Family Research InstituteUniversity of British Columbia
Fundersnot available
KeywordsAffect (linguistics)FetusUterine arteryPhysiologyGestationPregnancyAnxietyMoodMedicineDepression (economics)Internal medicineObstetricsPsychologyEndocrinologyBiologyClinical psychologyPsychiatry
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: While research has shown that maternal mood (depression and/or anxiety) can have effects on the fetus, little is known about whether maternal positive and negative affect influences the fetus. METHOD: We examined fetal vascular and heart rate changes at 36 weeks gestation in 53 euthymic mothers according to their Positive and Negative Affect Scale (PANAS) scores. RESULTS: Mothers who reported high levels of negative affect showed reduced uterine artery flow, decreased fetal heart rate (fHR) variability, an altered diurnal pattern, and decreased uterine artery cross-sectional area compared to mothers who reported low levels of negative affect. Mothers with low positive affect had a steeper diurnal pattern in fHR accelerations and decreased uterine artery mean velocity flow than mothers with high positive affect. LIMITATIONS: Our observational study suffers from a small sample size. CONCLUSION: Even in the absence of an Axis I Major Depressive Disorder (MDD), variations in maternal affect appear to be associated with variations in fetal and uterine physiology.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.013
GPT teacher head0.276
Teacher spread0.264 · 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 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

Citations5
Published2014
Admission routes1
Has abstractyes

Explore more

Same venuePubMed→Same topicMaternal Mental Health During Pregnancy and Postpartum→French-language works237,207→