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Record W4304588443 · doi:10.1037/fam0001026

Dyadic coping trajectories across the transition to parenthood: Associations with child mental health problems.

2022· article· en· W4304588443 on OpenAlexaff
Céline Stadelmann, Mirjam Senn, Fabienne Forster, Valentina Rauch-Anderegg, Fridtjof W. Nußbeck, Matthew D. Johnson, Alexandra Iwanski, Peter Zimmermann, Guy Bodenmann

Bibliographic record

VenueJournal of Family Psychology · 2022
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsUniversity of Alberta
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsPsychologyPsycINFOMental healthCoping (psychology)Developmental psychologyClinical psychologyPsychiatryMEDLINE

Abstract

fetched live from OpenAlex

= 288 couples) at seven points of measurement (27th, 32nd week of pregnancy, 2nd, 14th, 40th week postpartum, 3- and 4-year postpartum) and children's emotional and behavioral problems from parent report (4-year postpartum) were used. Latent class growth analyses revealed that over half of the couples experienced a moderate decline in positive DC across the TTP (58%), whereas only fathers reported a decline among the remaining couples (42%). Fathers with a partner who maintained their level of positive DC reported more child emotional and behavioral problems than fathers whose partners' DC also decreased. Results for negative DC indicated two subgroups in which one partner maintained their initial level of negative DC (stable fathers: 10%, stable mothers: 23%), while the other increased. In most couples, both parents increased their negative DC (67%). Fathers reported more child emotional and behavioral problems if their negative DC increased across the TTP than if their negative DC remained stable regardless of the negative DC of their partner. The existence of different DC trajectory patterns needs to be considered in further research as well as prevention. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.534

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.373
Teacher spread0.333 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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