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Record W4389444497 · doi:10.5964/ijpr.9771

Attachment, Relational Maintenance Behaviors and Relationship Quality in Romantic Long-Distance Relationships: A Dyadic Perspective

2023· article· en· W4389444497 on OpenAlexaff
Geneviève Bouchard, Madeleine Gaudet, Gabrielle Cloutier, Myriam J. Martín

Bibliographic record

VenueInterpersona An International Journal on Personal Relationships · 2023
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsDyadClosenessPsychologyMediationPerspective (graphical)Path analysis (statistics)RomancePositive relationshipAttachment theoryDevelopmental psychologyQuality (philosophy)Social psychology

Abstract

fetched live from OpenAlex

This study tested an actor-partner interdependence mediation model (APIMeM) in which dyadic relational maintenance behaviors (RMBs) mediate the relationship between romantic attachment (i.e., anxious and avoidant) and multiple indicators of relationship quality among couples in long-distance relationships (LDRs). Data were collected from 137 couples (women’s mean age = 20.37 years; men’s mean age = 21.93) who were in a serious romantic LDR and who completed an attachment measure, a measure of dyadic RMBs, and four measures of relationship quality (i.e., relationship satisfaction, relational commitment, closeness with the partner, and connection with others). Path analyses revealed significant actor and partner effects. Moreover, a total mediation between women’s anxious attachment and both partners’ relationship quality, and a partial mediation between men’s and women’s avoidant attachment and their own relationship quality were uncovered. Overall, the results suggest that, for couples in LDRs, one partner’s behaviors, cognitions, or emotions influence each member of the dyad as well as the quality of the relationship.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0010.001

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 head0.106
GPT teacher head0.450
Teacher spread0.344 · 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.

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

Citations9
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueInterpersona An International Journal on Personal RelationshipsSame topicAttachment and Relationship DynamicsFrench-language works237,207