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Record W4310641967 · doi:10.1111/jmft.12621

Is it really that important to you? How the topics of conflict and emotional reactions to conflicts explain the associations between attachment insecurities and relationship satisfaction

2022· article· en· W4310641967 on OpenAlexafffund
Caroline Dugal, Audrey Brassard, Yvan Lussier, Katherine Péloquin

Bibliographic record

VenueJournal of Marital and Family Therapy · 2022
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversité du Québec à Trois-RivièresUniversité de MontréalUniversité de Sherbrooke
FundersSocial Sciences and Humanities Research Council of CanadaFonds de Recherche du Québec-Société et Culture
KeywordsPsychologySocial psychologyPerceptionPath analysis (statistics)Anxiety

Abstract

fetched live from OpenAlex

This study sought to examine the intermediary roles of different topics of conflict and negative emotions following conflicts in the associations between attachment insecurities and relationship satisfaction in a sample of 253 mixed-gender couples from the community. Results from path analyses based on the Actor-Partner Interdependence Model showed that attachment anxiety and attachment avoidance were associated with the perception, in both partners, of experiencing more conflicts in the relationship. In turn, the more participants perceived conflicts related to major issues and daily annoyances, the more they reported negative emotions following conflicts and lower relationship satisfaction. Participants' report of conflicts related to major issues was also related to their partner's lower relationship satisfaction. Findings highlight the significance of accounting for the topics on which couples argue and of using an attachment-based framework to help couples deal with the negative emotions that they experience following conflicts.

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)0.002
metaresearch head score (Gemma)0.013
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 score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.103
GPT teacher head0.368
Teacher spread0.265 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

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