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Arginine vasopressin 1a receptor gene and maternal behavior: evidence of association and moderation

2012· article· en· W2078202416 on OpenAlexafffund
Rossana Bisceglia, Jennifer M. Jenkins, K. Wigg, Thomas G. O’Connor, Greg Moran, Cathy L. Barr

Bibliographic record

VenueGenes Brain & Behavior · 2012
Typearticle
Languageen
FieldPsychology
TopicNeuroendocrine regulation and behavior
Canadian institutionsWestern UniversitySickKids FoundationHospital for Sick ChildrenUniversity Health NetworkUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsModerationMaternal sensitivitySiblingAssociation (psychology)VasopressinPsychologyAlleleDevelopmental psychologyVasopressin receptorArginineGeneClinical psychologyMedicineGeneticsEndocrinologyBiologyAmino acidSocial psychology

Abstract

fetched live from OpenAlex

This study examined associations among maternal sensitivity, mothers' early adversity and the Arginine Vasopressin 1a Receptor (AVPR1A) gene. Early adversity in mothers' background has been found to be associated with lower maternal sensitivity. Animal literature suggests that variation in the AVPR1A gene is associated with parenting quality. The goal of the study was to examine the role of the AVPR1A gene in maternal sensitivity, especially under conditions of high early adversity. Participants included 151 Caucasian women from a community sample. The women were videotaped in their home while interacting separately with two of their children (target child = 18 months, older sibling <6 years). Evidence was found for an association between the AVPR1A gene and maternal sensitivity. Mothers with two copies of the long RS3 alleles were less sensitive than mothers with one or zero copies of the long alleles. This association was strongest under conditions of high maternal early adversity.

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)0.002
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0020.000

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 head0.053
GPT teacher head0.355
Teacher spread0.302 · 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

Citations49
Published2012
Admission routes2
Has abstractyes

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