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Record W2912145360 · doi:10.1017/jrr.2018.24

Does Love Matter to Infants' Health: Influence of Maternal Attachment Representations on Reports of Infant Health

2019· article· en· W2912145360 on OpenAlexaff
Elaine Scharfe, Nicole Paradise Black

Bibliographic record

VenueJournal of Relationships Research · 2019
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsTrent University
Fundersnot available
KeywordsAnxietyDepression (economics)PregnancyMedicineDevelopmental psychologyPsychologyTemperamentPsychiatryPersonality

Abstract

fetched live from OpenAlex

Although there is considerable support for the influence of maternal attachment on children's development (see Gerhardt, 2015), this is one of the first studies to examine the effects of maternal prenatal reports of attachment representations with close others on reports of infants’ health. Mothers (N = 483) completed surveys to assess attachment and depression in the second or third trimester of pregnancy, infants’ health over the first 6 months, and depression and infant temperament when infants were 6 months old. We found that insecure mothers, as compared to secure mothers, were more likely to report that their infants experienced colic and illnesses associated with immune, cardiovascular, and respiratory systems. It may be that secure mothers experience less anxiety associated with parenting and, as expected, were consistently found to report lower levels of infant illness symptoms. Alternatively, secure mothers would be expected to provide more consistent and responsive care compared to insecure mothers, which may also influence their infants’ physical health (see also Gerhardt, 2015). Future research needs to further explore this finding — do secure mothers simply perceive their infants to be healthier due to their own low anxiety or are infants of secure mothers healthier due to consistent and responsive care received?

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)0.006
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 score0.020
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.075
GPT teacher head0.500
Teacher spread0.426 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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