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Record W2109934609 · doi:10.1002/dev.21085

Prenatal maternal stress affects motor function in 5½‐year‐old children: Project Ice Storm

2012· article· en· W2109934609 on OpenAlexafffund
Xiujing Cao, David P. Laplante, Alain Brunet, Antonio Ciampi, Suzanne King

Bibliographic record

VenueDevelopmental Psychobiology · 2012
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsMcGill UniversityDouglas Mental Health University InstituteDouglas College
FundersCanadian Institutes of Health Research
KeywordsDistressPsychologyOffspringMotor functionPregnancyPrenatal stressMotor skillDevelopmental psychologyClinical psychologyAudiologyMedicinePhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

Evidence suggests that prenatal maternal stress (PNMS) has long-term effects on several outcomes, yet effects on neuromotor function are relatively unknown. We aimed to determine whether disaster-related PNMS predicts motor functioning in young children and whether timing of exposure and sex of the child moderate these effects. Objective and subjective PNMS levels were assessed among pregnant women exposed to a natural disaster. Their children's bilateral coordination, balance, and visual motor integration (VMI) were assessed at 5½ years. Girls performed better than boys. Objective stress exposure and subjective distress interacted such that when subjective distress was high, no added effect of objective hardship was observed; when subjective distress was low, objective hardship showed a negative effect. In girls, late pregnancy exposure was associated with poorer outcomes. In conclusion, disaster-related PNMS is associated with relatively lower motor functions in exposed offspring. Exposure timing, sex, and type of stress influenced the effects.

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)0.001
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.018
GPT teacher head0.293
Teacher spread0.274 · 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

Citations92
Published2012
Admission routes2
Has abstractyes

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