MétaCan
Menu
Back to cohort
Record W4224274098 · doi:10.3138/jmvfh-2021-0108

Adult attachment and spousal reports of conflict and quality of partner interactions during a post-deployment reunion

2022· article· en· W4224274098 on OpenAlexaffvenueabout
Valerie M. Wood, Linna Tam‐Seto, Tara K. MacDonald, Samantha Urban

Bibliographic record

VenueJournal of Military Veteran and Family Health · 2022
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsDepartment of National DefenceMcMaster UniversityQueen's University
Fundersnot available
KeywordsClosenessSoftware deploymentAbandonment (legal)FeelingAnxietyPsychologySocial psychologyAttachment theoryPsychiatryPolitical scienceEngineering

Abstract

fetched live from OpenAlex

LAY SUMMARY The goal of this study was to understand whether spousal attachment is related to the quality of post-deployment interactions and issues of conflict reported by spouses of Canadian Armed Forces (CAF) members during a post-deployment reunion. A total of 104 spouses of CAF Regular Force personnel who had recently been reunited with their partners after a deployment were surveyed. Results showed that both attachment anxiety (fear of rejection and abandonment) and attachment avoidance (discomfort with emotional intimacy and closeness) were related to lower-quality post-deployment interactions and the reported frequency of particular conflict issues. Specifically, attachment anxiety was related to more reports of unmet emotional needs, difficulties re-establishing intimacy, finances, and being less likely to describe the conflict in positive terms. The relationship between attachment anxiety and the quality of post-deployment interactions was explained by feeling as though one’s original expectations of the reunion were not met.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.061
GPT teacher head0.427
Teacher spread0.366 · 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

Citations0
Published2022
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Military Veteran and Family HealthSame topicAttachment and Relationship DynamicsFrench-language works237,207