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Record W3175274914 · doi:10.1177/0192513x211026946

Changes in Attachment and Commitment in Couples Transitioning to Parenthood

2021· article· en· W3175274914 on OpenAlexafffund
Rose Lapolice Thériault, Audrey Brassard, Anne‐Sophie Gingras, Anne Brault‐Labbé, Marie‐France Lafontaine, Katherine Péloquin

Bibliographic record

VenueJournal of Family Issues · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Dynamics and Relationships
Canadian institutionsUniversity of OttawaUniversité de MontréalUniversité de Sherbrooke
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologySocial psychologyDevelopmental psychologyAttachment theory

Abstract

fetched live from OpenAlex

This study examined whether attachment predicts changes in commitment and whether commitment predicts changes in attachment in both partners during the transition to parenthood. Both partners of 93 couples completed online questionnaires individually at the second trimester of pregnancy and at 4 months postpartum. Autoregressive cross-lagged path analyses based on the Actor-Partner Interdependence Model tested the bidirectional associations between attachment dimensions (anxiety and avoidance) and three modes of commitment (optimal, over-commitment, and under-commitment). Results revealed that for both partners, prenatal attachment avoidance was associated with a decrease in optimal commitment and an increase in under-commitment from pre- to postpartum. Fathers' attachment anxiety was associated with a decrease in mothers' under-commitment. Furthermore, prenatal optimal commitment was associated with a decrease in attachment avoidance, whereas under-commitment was associated with an increase in attachment avoidance. Fathers' prenatal over-commitment was associated with an increase in their own attachment anxiety and avoidance. These results highlight how attachment insecurities and relationship commitment interrelate during this major transition.

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 score0.062
Threshold uncertainty score0.956

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 head0.059
GPT teacher head0.368
Teacher spread0.308 · 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
Published2021
Admission routes2
Has abstractyes

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