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Record W4294128797 · doi:10.1111/jmft.12608

“We're in this together”: Attachment insecurities, dyadic coping strategies, and relationship satisfaction in couples involved in medically assisted reproduction

2022· article· en· W4294128797 on OpenAlexafffund
Katherine Péloquin, Stéphanie Boucher, Zoé Benoit, Mireille Jean, Laurie Beauvilliers, Belina Carranza‐Mamane, Audrey Brassard

Bibliographic record

VenueJournal of Marital and Family Therapy · 2022
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversité de SherbrookeUniversité de Montréal
FundersFonds de Recherche du Québec - SantéFonds de Recherche du Québec-Société et Culture
KeywordsPsychologyInsecure attachmentCoping (psychology)Path analysis (statistics)Attachment theoryAnxietyClinical psychologyVulnerability (computing)Developmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

Attachment is an important predictor of relationship satisfaction and attachment insecurities are a risk factor for couples under stress. Drawing from the Vulnerability-Stress-Adaptation model, we investigated whether dyadic coping strategies (DCS) would explain the links between attachment insecurities and relationship satisfaction in 97 couples involved in medically assisted reproduction (MAR). Path analyses revealed that for women and men, attachment insecurities (anxiety, avoidance) were associated with their own lower relationship satisfaction through their lower use of positive DCS. Attachment avoidance was also associated with participants' own lower relationship satisfaction through their own lower use of negative DCS. Men's attachment avoidance was also related to their partner's lower relationship satisfaction via their own and their partner's lower use of positive DCS. The findings suggest that promoting the use of positive DCS may be important to preserve relationship satisfaction in couples involved in MAR.

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 score0.021
Threshold uncertainty score0.476

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 integrity0.0000.001
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 head0.055
GPT teacher head0.348
Teacher spread0.293 · 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

Citations12
Published2022
Admission routes2
Has abstractyes

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